{"pdf_plumber": {"text": "Conselho de \u00c9tica de IA da IBM\nModelos de base:\noportunidades, riscos\ne mitiga\u00e7\u00f5esAtribui\u00e7\u00e3o\nCom gratid\u00e3o aos patrocinadores executivos do grupo de trabalho\nda \u00e9tica em IA, Christina Montgomery e Francesca Rossi, e \u00e0s\ncontribui\u00e7\u00f5es dos membros do grupo de trabalho Betsy Greytok, Bryan\nBortnick, Catherine Quinlan, David Piorkowski, Eniko Rozsa, Heather\nDomin, Heather Gentile, Jamie VanDodick, Jill Maguire, John McBroom,\nJoshua New, Justin Weisz, Katherine Fick, Kevin Black, Kush Varshney,\nManish Bhide, Manish Goyal, Melis Kiziltay, Michael Epstein, Michael\nHind, Milena Pribic, Phaedra Boinodiris, Rogerio Abreu de Paula,\nSaishruthi Swaminathan e Suj Perepa.\n2 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\u00cdndice\n04 16\nExecutivo Risco\nResumo Exemplos\n05 24\nIntrodu\u00e7\u00e3o Princ\u00edpios, pilares\ne governan\u00e7a\n06 25\nBenef\u00edcios dos Prote\u00e7\u00f5es\nmodelos de base e mitiga\u00e7\u00f5es\n08 27\nRiscos dos Pol\u00edticas, regulamenta\u00e7\u00f5es\nmodelos de base e melhores pr\u00e1ticas de IA\nExemplos\n3Resumo executivo\nA ascens\u00e3o dos modelos de base oferece \u00e0s empresas novas e Neste documento:\nempolgantes possibilidades, mas tamb\u00e9m levanta quest\u00f5es novas e\namplas sobre design, desenvolvimento, implementa\u00e7\u00e3o e uso \u00e9tico.\nSegundo uma recente pesquisa sobre IA generativa do IBM Institute for Exploraremos as vantagens dos modelos de base, incluindo\nBusiness Value, as organiza\u00e7\u00f5es j\u00e1 est\u00e3o manifestando preocupa\u00e7\u00f5es sua capacidade de realizar tarefas desafiadoras, potencial\nsobre quest\u00f5es relacionadas \u00e0 confian\u00e7a, especificamente como para acelerar a ado\u00e7\u00e3o de IA, habilidade de aumentar\nbarreiras para investimentos. Suas principais preocupa\u00e7\u00f5es s\u00e3o a produtividade e os benef\u00edcios econ\u00f4micos que eles\nciberseguran\u00e7a (57%), privacidade (51%) e precis\u00e3o (47%). Muitas proporcionam.\norganiza\u00e7\u00f5es estavam levando essas preocupa\u00e7\u00f5es a s\u00e9rio antes\nda \u2018consumeriza\u00e7\u00e3o\u2019 da IA generativa, expressando sua inten\u00e7\u00e3o de\ninvestir pelo menos 40% mais em \u00e9tica de IA nos pr\u00f3ximos tr\u00eas anos. Discutiremos as tr\u00eas categorias de risco, incluindo riscos\nA conscientiza\u00e7\u00e3o sobre riscos e poss\u00edveis maneiras de mitig\u00e1-los \u00e9 o conhecidos de formas anteriores de IA, riscos conhecidos\nprimeiro passo crucial para a cria\u00e7\u00e3o de sistemas de IA confi\u00e1veis. amplificados por modelos de base e riscos emergentes\nintr\u00ednsecos aos recursos generativos dos modelos de base.\nAbordaremos os princ\u00edpios, os pilares e o controle que\nformam a base das iniciativas \u00e9ticas de IA da IBM e\nsugeriremos barreiras para a mitiga\u00e7\u00e3o de riscos.\n4 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Introdu\u00e7\u00e3o\n\u00c0 medida que o uso de IA continua se expandindo, os grandes e A IBM \u00e9 uma empresa de nuvem h\u00edbrida e IA com vasta reputa\u00e7\u00e3o como\ncomplexos modelos de IA est\u00e3o fornecendo resultados promissores administradora de dados respons\u00e1vel e comprometida com a \u00e9tica em\nde desempenho, bem como resolvendo alguns dos problemas mais IA. Usando a capacidade de nossas equipes de pesquisa, produto e\ndesafiadores da sociedade. No entanto, criar grandes conjuntos de dados consultoria, juntamente com parceiros externos, como a Hugging Face,\nde treinamento e modelos complexos para cada aplicativo de IA pode ajudamos a trazer o poder dos modelos de base para nossos clientes\nser extremamente dif\u00edcil para as empresas. Modelos de base fornecem e a criar IAs confi\u00e1veis em qualquer empresa. A IBM tamb\u00e9m continua\num caminho para alcan\u00e7ar o melhor dos dois mundos: desenvolver investindo na cria\u00e7\u00e3o de novas plataformas, como a IA IBM watsonx\nmodelos de \u00faltima gera\u00e7\u00e3o poderosos e reutiliz\u00e1-los diretamente ou e plataformas e tecnologias de dados, para projetar e desenvolver\naplicar m\u00e9todos de ajuste para implementar uma variedade de casos modelos de IA para se comportar de maneira audit\u00e1vel e confi\u00e1vel.\nde uso, em vez de treinar novos modelos para cada caso de uso. Por\nexemplo, a IBM Research desenvolveu modelos de base para inspe\u00e7\u00e3o Este documento descreve o ponto de vista da IBM sobre a \u00e9tica dos\nvisual. Esses modelos de base aprendem a representa\u00e7\u00e3o geral de modelos de base. \u00c9 a primeira vers\u00e3o, e as vers\u00f5es futuras expandir\u00e3o\nsuperf\u00edcies e corredores de concreto e podem ser ajustados ainda v\u00e1rios aspectos da abordagem \u00e9tica do modelo de base da IBM.\nmais para casos de uso espec\u00edficos, como detec\u00e7\u00e3o de rachaduras ou Esperamos que este documento seja \u00fatil para todos os stakeholders no\ninspe\u00e7\u00e3o de defeitos com dados menos rotulados. desenvolvimento, implementa\u00e7\u00e3o e uso do modelo de base de forma\nrespons\u00e1vel.\nA IBM define um modelo de base como um modelo de IA que pode\nser adaptado a uma ampla gama de tarefas de recebimento de dados.\nOs modelos de base normalmente s\u00e3o modelos generativos de grande\nescala treinados em dados n\u00e3o rotulados usando autossupervis\u00e3o.\nComo modelos de grande escala, os modelos de base podem incluir\nbilh\u00f5es de par\u00e2metros.\n5 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Benef\u00edcios dos\nmodelos de base\nOs modelos de base podem melhorar significativamente o processo de Maior produtividade\ndesenvolvimento de sistemas de IA e auxiliar no avan\u00e7o da IA da fase de A natureza generativa dos modelos de base amplia o n\u00famero de\nexplora\u00e7\u00e3o para a ado\u00e7\u00e3o nas empresas. Seus benef\u00edcios incluem: \u00e1reas em que a IA pode ser usada em uma empresa para ajudar a\nmelhorar a produtividade, automatizando tarefas rotineiras e tediosas\nRealizar tarefas complexas e permitindo que os usu\u00e1rios dediquem mais tempo ao trabalho\nModelos de base mostram um aumento significativo no desempenho criativo e inovador. Por exemplo, o IBM Watsonx Code Assistant,\nna resolu\u00e7\u00e3o de problemas complexos e dif\u00edceis. Por exemplo, desenvolvido com modelos de base, possibilita que desenvolvedores,\no modelo de base geoespacial da colabora\u00e7\u00e3o IBM e NASA foi independentemente do n\u00edvel de experi\u00eancia, escrevam c\u00f3digos usando\nprojetado para converter os dados de sat\u00e9lite da NASA em mapas recomenda\u00e7\u00f5es geradas por IA.\nde desastres naturais, como inunda\u00e7\u00f5es e outras mudan\u00e7as de\ncen\u00e1rio. O modelo tamb\u00e9m pode ser usado para ajudar a revelar Time to value mais r\u00e1pido\no passado do nosso planeta; estimar riscos para culturas, empresas Modelos de base geralmente s\u00e3o treinados com dados n\u00e3o rotulados,\nou infraestruturas devido ao clima severo; desenvolver estrat\u00e9gias que est\u00e3o mais dispon\u00edveis em grandes quantidades do que dados\npara se adaptar \u00e0s mudan\u00e7as clim\u00e1ticas; e auxiliar no agroneg\u00f3cio. rotulados. Uma vez treinados, os modelos de base podem ser usados\nO modelo est\u00e1 planejado para ser disponibilizado previamente aos diretamente ou ap\u00f3s serem ajustados para aplicativos de recebimento\nclientes IBM por meio do IBM Environmental Intelligence Suite. de dados, usando uma pequena quantidade de dados rotulados\nespecializados, que podem diminuir a cria\u00e7\u00e3o do time to value.\nPara ilustrar, o MoLFormer-XL da IBM \u00e9 um modelo de base que\n\u00e9 capaz de inferir a estrutura de mol\u00e9culas a partir de representa\u00e7\u00f5es\nsimples, tornando mais f\u00e1cil a aprendizagem de v\u00e1rias tarefas\nde recebimento de dados, como prever as propriedades f\u00edsicas\ne qu\u00e2nticas de uma mol\u00e9cula, identificar mol\u00e9culas semelhantes,\nrastrear mol\u00e9culas j\u00e1 aprovadas para novos casos de uso e descobrir\nnovas mol\u00e9culas. Moderna e IBM est\u00e3o explorando formas de\nusar o MoLExer para ajudar a prever propriedades das mol\u00e9culas e\nentender as caracter\u00edsticas de poss\u00edveis medicamentos de mRNA.\n6 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024A IBM permite que as empresas\nUtilize diversas modalidades de dados criem e detenham o valor de\nOs modelos de base podem ser treinados usando diversas modalidades\nmodelos de base para seus\nde dados, como l\u00edngua natural, texto, imagem e \u00e1udio. Eles tamb\u00e9m\nneg\u00f3cios, trazendo as melhores\npodem ser aplicados a tarefas que exigem diferentes tipos de dados,\ncomo dados de s\u00e9ries temporais, dados geoespaciais, dados tabulares, inova\u00e7\u00f5es da comunidade de\ndados semiestruturados e dados de modalidade mista, como texto\nIA aberta e global, operando de\ncombinado com imagens.\nforma eficiente em ambientes de\nDespesas amortizadas computa\u00e7\u00e3o h\u00edbrida, ajudando\nEmbora o custo inicial do treinamento de um modelo de base seja\na mitigar riscos e controlando\nsignificativamente maior do que o treinamento de um modelo de IA\nrigorosamente a IA.\ntradicional, o custo adicional para aplic\u00e1-lo em uma nova tarefa \u00e9\nconsideravelmente menor. O uso de modelos de base pr\u00e9-treinados\npoderia ajudar a eliminar a necessidade de que as empresas fa\u00e7am\ninvestimentos substanciais para treinar modelos de base e explorar suas\nnovas capacidades. Para uma empresa, a confiabilidade dos modelos,\na efici\u00eancia energ\u00e9tica, o desempenho, a portabilidade e a capacidade\nde usar dados corporativos de forma eficaz e segura s\u00e3o fundamentais.\n7 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Riscos dos\nmodelos de base\nComo todas as tecnologias que avan\u00e7am rapidamente, os modelos\nde base oferecem riscos e benef\u00edcios. Alguns s\u00e3o riscos legais,\ncomo restri\u00e7\u00f5es \u00e0 movimenta\u00e7\u00e3o ou uso de dados, e precisam\nser cuidadosamente avaliados de acordo com a legisla\u00e7\u00e3o atual e\nem evolu\u00e7\u00e3o. Outros riscos t\u00eam uma natureza \u00e9tica e devem ser\nconsiderados cuidadosamente para que a tecnologia tenha um impacto\npositivo. Em geral, os riscos de IA levantam quest\u00f5es sociot\u00e9cnicas\ne devem ser abordados e mitigados por meio de m\u00e9todos sociot\u00e9cnicos,\nincluindo ferramentas de software, processos de avalia\u00e7\u00e3o de risco,\nframeworks de \u00e9tica em IA, mecanismos de controle, consultas\nmultistakeholder, padr\u00f5es e regulamenta\u00e7\u00e3o. Iremos listar os riscos\nconsiderando as seguintes 3 categorias:\n1. Tradicional. Riscos conhecidos de formas anteriores ou anteriores\nde sistemas de IA\n2. Amplificados. Riscos conhecidos, mas agora intensificados devido \u00e0s\ncaracter\u00edsticas intr\u00ednsecas dos modelos de base, principalmente seus\nrecursos generativos inerentes\n3. Novo. Riscos emergentes intr\u00ednsecos aos modelos de base e suas\ncapacidades generativas inerentes\nTamb\u00e9m estruturamos a lista de riscos em rela\u00e7\u00e3o a se est\u00e3o\nprincipalmente associados ao conte\u00fado fornecido ao modelo\nbase, o input, ou ao conte\u00fado gerado por ele, o output, ou se est\u00e3o\nrelacionados a desafios adicionais.\n8 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 20241. Riscos associados \u00e0 entrada\nFase de treinamento e ajuste\nGrupo Risco Por que isso \u00e9 uma preocupa\u00e7\u00e3o? Indicador\nJusti\u00e7a Vi\u00e9s de dados: vi\u00e9s hist\u00f3rico, representacional Treinar um sistema de IA com dados enviesados, como vi\u00e9s hist\u00f3rico ou Amplificado\ne social presente nos dados usados para representacional, pode resultar em outputs enviesados ou distorcidos\ntreinar e fazer o ajuste fino do modelo. que podem representar injustamente ou discriminar certos grupos\nou indiv\u00edduos. Al\u00e9m dos impactos negativos na sociedade, entidades\ncomerciais podem enfrentar consequ\u00eancias legais, interrup\u00e7\u00e3o\ndas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o decorrentes dos resultados\nenviesados do modelo.\nRobustez Envenenamento de dados: um tipo de ataque O envenenamento de dados pode tornar o modelo sens\u00edvel a um padr\u00e3o Tradicional\nadversarial no qual um advers\u00e1rio ou agente de dados malicioso e produzir o output desejado pelo advers\u00e1rio. Isso\ninterno malicioso injeta intencionalmente pode criar um risco de seguran\u00e7a onde advers\u00e1rios podem manipular\namostras corrompidas, falsas, enganosas o comportamento do modelo em seu pr\u00f3prio benef\u00edcio. Al\u00e9m de produzir\nou incorretas no conjunto de dados de resultados n\u00e3o intencionais e potencialmente maliciosos, uma diverg\u00eancia\ntreinamento ou ajuste fino. do modelo causada por envenenamento de dados pode resultar em\nentidades comerciais enfrentando consequ\u00eancias legais, interrup\u00e7\u00e3o\ndas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\nAlinhamento Curadoria de dados: quando os dados de Uma curadoria de dados inadequada pode afetar adversamente como Amplificado\nde valor treinamento ou ajuste s\u00e3o coletados ou um modelo \u00e9 treinado, resultando em um modelo que n\u00e3o se comporta\npreparados de forma inadequada. de acordo com os valores pretendidos. Exemplos de uma curadoria de\ndados inadequada podem incluir erros de rotulagem ou anota\u00e7\u00e3o nos\ndados usados para treinar ou ajustar o modelo. Corrigir problemas ap\u00f3s\no treinamento e a implementa\u00e7\u00e3o do modelo pode ser insuficiente para\ngarantir um comportamento adequado. Um comportamento inadequado do\nmodelo pode resultar em entidades comerciais enfrentando consequ\u00eancias\nlegais, interrup\u00e7\u00f5es nas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\nRetreinamento baseado em downstream: O reaproveitamento de output downstream para treinar novamente um Novo\nusando de outputs indesej\u00e1veis (imprecisos, modelo sem implementar a verifica\u00e7\u00e3o humana adequada aumenta as\ninadequados, conte\u00fado do usu\u00e1rio, etc.) chances de que outputs indesej\u00e1veis sejam incorporados aos dados de\nde aplica\u00e7\u00f5es downstream para fins de treinamento ou ajuste do modelo, possivelmente gerando outputs ainda\nretreinamento. mais indesej\u00e1veis. Comportamento inadequado do modelo pode resultar\nem entidades empresariais enfrentando consequ\u00eancias legais ou danos\n\u00e0 reputa\u00e7\u00e3o. N\u00e3o cumprir com as leis de transfer\u00eancia de dados pode\nresultar em multas e outras consequ\u00eancias legais.\nLeis de dados Transfer\u00eancia de dados: leis e outras Restri\u00e7\u00f5es \u00e0 transfer\u00eancia de dados podem afetar a disponibilidade dos Tradicional\nrestri\u00e7\u00f5es podem limitar ou proibir a dados necess\u00e1rios para treinar um modelo de IA e podem resultar em\ntransfer\u00eancia de dados. dados mal representados. Al\u00e9m do impacto na disponibilidade de dados,\no n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de transfer\u00eancia de dados\npode resultar em multas e outras consequ\u00eancias legais.\nUso de dados: leis e outras restri\u00e7\u00f5es podem O n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de uso de dados pode Tradicional\nlimitar ou proibir o uso de alguns dados para resultar em multas e outras consequ\u00eancias legais.\ncasos de uso espec\u00edficos de IA.\nAquisi\u00e7\u00e3o de dados: leis e outras O n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es da aquisi\u00e7\u00e3o de dados Amplificado\nregulamenta\u00e7\u00f5es podem limitar a coleta pode resultar em multas e outras consequ\u00eancias legais.\nde certos tipos de dados para casos de uso\nespec\u00edficos de IA.\n9 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Grupo Risco Por que isso \u00e9 uma preocupa\u00e7\u00e3o? Indicador\nPropriedade Direitos de uso de dados: termos de servi\u00e7o, As leis e regulamenta\u00e7\u00f5es referentes ao uso de dados para treinar IA Amplificado\nintelectual leis de direitos autorais, conformidade com s\u00e3o inst\u00e1veis e podem variar de pa\u00eds para pa\u00eds, o que cria desafios no\nlicen\u00e7as ou outras quest\u00f5es de propriedade desenvolvimento de modelos. Se o uso de dados violar regras ou restri\u00e7\u00f5es,\nintelectual podem restringir a capacidade as entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\nde usar certos dados para a constru\u00e7\u00e3o interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nde modelos.\nTranspar\u00eancia Transpar\u00eancia de dados: desafio em A transpar\u00eancia dos dados \u00e9 importante para a conformidade legal e \u00e9tica Amplificado\ndocumentar como os dados de um modelo da IA. A falta de informa\u00e7\u00f5es limita a capacidade de avaliar os riscos\nforam coletados, curados e utilizados associados aos dados. A falta de requisitos padronizados pode limitar a\npara trein\u00e1-lo. divulga\u00e7\u00e3o, pois as organiza\u00e7\u00f5es protegem segredos comerciais e tentam\nevitar que outros copiem seus modelos.\nProced\u00eancia dos dados: desafio em Nem todas as fontes de dados s\u00e3o confi\u00e1veis. Os dados podem ter sido Amplificado\npadronizar e estabelecer m\u00e9todos para coletados, manipulados ou falsificados de forma anti\u00e9tica. O uso de dados\nverificar de onde os dados vieram. n\u00e3o confi\u00e1veis pode resultar em comportamentos indesej\u00e1veis no modelo.\nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPrivacidade Informa\u00e7\u00f5es pessoais nos dados: inclus\u00e3o Se n\u00e3o desenvolvido adequadamente para proteger dados sens\u00edveis, Tradicional\nou presen\u00e7a de informa\u00e7\u00f5es pessoalmente o modelo pode expor informa\u00e7\u00f5es pessoais no output gerado. Al\u00e9m disso,\nidentific\u00e1veis (PII) e informa\u00e7\u00f5es pessoais dados pessoais ou sens\u00edveis devem ser revisados e tratados de acordo\nsens\u00edveis (SPI) nos dados usados para treinar com as leis e regulamenta\u00e7\u00f5es de privacidade. As entidades empresariais\nou ajustar o modelo. podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es\ne outras consequ\u00eancias legais se forem encontradas em viola\u00e7\u00e3o.\nReidentifica\u00e7\u00e3o: mesmo com a remo\u00e7\u00e3o de Os dados que podem revelar informa\u00e7\u00f5es pessoais ou sens\u00edveis devem Tradicional\ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es de privacidade, pois\n(PII) e informa\u00e7\u00f5es pessoais sens\u00edveis (SPI) as entidades comerciais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\ndos dados, ainda pode ser poss\u00edvel identificar interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se forem\npessoas devido a outros recursos dispon\u00edveis consideradas em viola\u00e7\u00e3o.\nnos dados.\nDireitos de privacidade de dados: desafios A identifica\u00e7\u00e3o ou uso inadequado de dados pode resultar em viola\u00e7\u00e3o das Amplificado\nrelacionados \u00e0 capacidade de fornecer leis de privacidade. O uso inadequado ou um pedido de remo\u00e7\u00e3o de dados\ndireitos do titular dos dados, como op\u00e7\u00e3o poderia obrigar as organiza\u00e7\u00f5es a reconfigurar o modelo, o que \u00e9 caro.\nde exclus\u00e3o, direito de acesso e direito ao Al\u00e9m disso, as entidades empresariais podem enfrentar multas, danos \u00e0\nesquecimento. reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se n\u00e3o\ncumprirem as regras e regulamenta\u00e7\u00f5es de privacidade de dados.\nConsentimento informado: dados Em algumas circunst\u00e2ncias, pode ser anti\u00e9tico coletar e usar dados Tradicional\ncoletados para treinar modelos de IA sem sem o consentimento da pessoa. Existem tamb\u00e9m poss\u00edveis riscos\no consentimento informado do propriet\u00e1rio, reputacionais associados a esse tipo de uso.\nmesmo quando legalmente permitido.\n10 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Infer\u00eancia Fase\nGrupo Risco Por que isso \u00e9 uma preocupa\u00e7\u00e3o? Indicador\nPrivacidade Informa\u00e7\u00f5es pessoais no prompt: divulgar Os dados do prompt podem ser armazenados ou posteriormente utilizados Novo\ninforma\u00e7\u00f5es pessoais ou informa\u00e7\u00f5es para outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos\npessoais sens\u00edveis como parte do prompt de dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es\nsolicita\u00e7\u00e3o enviada ao modelo. de privacidade. Sem um armazenamento e uso adequados dos dados,\nas entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPropriedade Informa\u00e7\u00f5es de IP no prompt: divulga\u00e7\u00e3o de Os dados do prompt podem ser armazenados ou posteriormente utilizados Novo\nintelectual informa\u00e7\u00f5es de direitos autorais ou outras para outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos\ninforma\u00e7\u00f5es de propriedade intelectual como de dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es\nparte do prompt enviado ao modelo. de propriedade intelectual. Sem um armazenamento e uso adequados\ndos dados, as entidades empresariais podem enfrentar multas, danos\n\u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nDados confidenciais no prompt: inclus\u00e3o de Se n\u00e3o for desenvolvido adequadamente para proteger dados confidenciais, Novo\ndados confidenciais como parte do prompt o modelo pode expor informa\u00e7\u00f5es confidenciais ou propriedade intelectual\nenviado ao modelo. no output gerado. Al\u00e9m disso, informa\u00e7\u00f5es confidenciais dos usu\u00e1rios finais\npodem ser coletadas e armazenadas inadvertidamente.\nRobustez Ataque de evas\u00e3o: tentativa de fazer com Os ataques de evas\u00e3o alteram o comportamento do modelo, geralmente Amplificado\nque um modelo produza outputs incorretos para beneficiar o atacante. Se os resultados de output n\u00e3o forem\nperturbando os dados enviados ao modelo devidamente considerados, as entidades empresariais podem enfrentar\ntreinado. multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras\nconsequ\u00eancias legais.\nAtaques baseados em prompt: ataques Dependendo do conte\u00fado revelado, as entidades empresariais podem Novo\nadversos, como inje\u00e7\u00e3o de prompt (tentativa enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras\nde for\u00e7ar um modelo a produzir um output consequ\u00eancias legais.\ninesperado), vazamento de prompt (tentativas\nde extrair o prompt do sistema de um\nmodelo), desbloqueio (tentativas de romper\nas prote\u00e7\u00f5es estabelecidas no modelo),\ne prepara\u00e7\u00e3o de prompt (tentativa de for\u00e7ar\num modelo a produzir um output alinhado\nao prompt).\n11 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 20242. Riscos associados \u00e0 sa\u00edda\nGrupo Risco Por que isso \u00e9 uma preocupa\u00e7\u00e3o? Indicador\nJusti\u00e7a Vi\u00e9s de output: o conte\u00fado gerado pode O vi\u00e9s pode prejudicar os usu\u00e1rios dos modelos de IA e amplificar Novo\nrepresentar injustamente certos grupos ou comportamentos discriminat\u00f3rios existentes. As entidades empresariais\nindiv\u00edduos. podem enfrentar danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras\nconsequ\u00eancias.\nVi\u00e9s de decis\u00e3o: quando um grupo \u00e9 O vi\u00e9s pode prejudicar as pessoas afetadas pelas decis\u00f5es do modelo. Tradicional\ninjustamente favorecido em rela\u00e7\u00e3o a outro As entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\ndevido aos efeitos das decis\u00f5es tomadas por interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nhumanos usando o output do modelo.\nPropriedade Viola\u00e7\u00e3o de direitos autorais: quando As leis e regulamenta\u00e7\u00f5es referentes ao uso de conte\u00fado que se assemelha Novo\nintelectual um modelo gera conte\u00fado que \u00e9 muito ou \u00e9 muito semelhante a outros dados protegidos por direitos autorais s\u00e3o\nsemelhante ou id\u00eantico a uma obra existente amplamente indefinidos e podem variar de pa\u00eds para pa\u00eds, o que representa\nprotegida por direitos autorais ou abrangida desafios na determina\u00e7\u00e3o e implementa\u00e7\u00e3o da conformidade. As entidades\npor um acordo de licen\u00e7a de c\u00f3digo aberto. empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das\nopera\u00e7\u00f5es e outras consequ\u00eancias legais.\nAlinhamento de Alucina\u00e7\u00e3o: gera\u00e7\u00e3o de conte\u00fado Outputs falsos podem induzir os usu\u00e1rios ao erro e serem incorporados Novo\nvalor factualmente impreciso ou n\u00e3o verdadeiro. em artefatos posteriores, propagando ainda mais a desinforma\u00e7\u00e3o. Isso\npode prejudicar tanto os propriet\u00e1rios quanto os usu\u00e1rios dos modelos de\nIA. Tamb\u00e9m, as entidades empresariais podem enfrentar multas, danos\n\u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nOutputs t\u00f3xicos: quando o modelo produz Conte\u00fado odioso, abusivo e profano (HAP) ou obsceno pode impactar Novo\nconte\u00fado odioso, abusivo e profano (HAP) ou adversamente e prejudicar as pessoas que interagem com o modelo.\nobsceno. Tamb\u00e9m, as entidades empresariais podem enfrentar multas, danos \u00e0\nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nConselhos perigosos: quando um modelo Uma pessoa pode agir com base em conselhos incompletos ou Novo\nfornece conselhos sem ter informa\u00e7\u00f5es preocupar-se com uma situa\u00e7\u00e3o que n\u00e3o se aplica a ela devido \u00e0 natureza\nsuficientes, resultando em poss\u00edveis perigos supergeneralizada do conte\u00fado gerado.\nse o conselho for seguido.\nUso indevido Dissemina\u00e7\u00e3o de desinforma\u00e7\u00e3o: utiliza\u00e7\u00e3o Espalhar desinforma\u00e7\u00e3o pode afetar a capacidade de uma pessoa Novo\nde um modelo para criar informa\u00e7\u00f5es de tomar decis\u00f5es informadas. As entidades empresariais podem\nenganosas ou falsas com o intuito de enganar enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es\nou influenciar um p\u00fablico-alvo. e outras consequ\u00eancias legais.\nToxicidade: utilizar um modelo para gerar Conte\u00fado t\u00f3xico pode ter um impacto negativo no bem-estar de seus Novo\nconte\u00fado odioso, abusivo e profano (HAP) destinat\u00e1rios. As entidades empresariais podem enfrentar multas, danos\nou obsceno. \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nUso n\u00e3o consensual: utilizar um modelo para Deepfakes podem disseminar desinforma\u00e7\u00e3o sobre uma pessoa, Amplificado\nimitar pessoas por meio de v\u00eddeo (deepfakes), possivelmente resultando em impactos negativos na reputa\u00e7\u00e3o da pessoa.\nimagens, \u00e1udio ou outras modalidades sem As entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\no consentimento delas. interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n12 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Grupo Risco Por que isso \u00e9 uma preocupa\u00e7\u00e3o? Indicador\nUso perigoso: utilizar um modelo com a \u00fanica As entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, Novo\ninten\u00e7\u00e3o de prejudicar pessoas. interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o revelar que o conte\u00fado A omiss\u00e3o do conte\u00fado produzido por IA pode ser interpretada como Novo\n\u00e9 gerado por um modelo de IA. enganosa, levando a uma diminui\u00e7\u00e3o da confian\u00e7a. A inten\u00e7\u00e3o de enganar\npode resultar na redu\u00e7\u00e3o da capacidade de a\u00e7\u00e3o humana, em multas,\ndanos \u00e0 reputa\u00e7\u00e3o e outras consequ\u00eancias legais.\nUso inadequado: utilizar um modelo para um Reutilizar um modelo sem compreender seus dados originais, inten\u00e7\u00e3o Amplificado\nfim para o qual o modelo n\u00e3o foi projetado. de design e objetivos pode resultar em comportamentos inesperados\ne indesejados do modelo.\nGera\u00e7\u00e3o Gera\u00e7\u00e3o de c\u00f3digo prejudicial: modelos A execu\u00e7\u00e3o de c\u00f3digo prejudicial pode abrir vulnerabilidades nos sistemas Novo\nde c\u00f3digo podem gerar c\u00f3digo que, quando executado, de TI. As entidades empresariais podem enfrentar multas, danos \u00e0\nprejudicial causa danos ou afeta inadvertidamente reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\noutros sistemas.\nConfian\u00e7a Excesso/falta de confian\u00e7a: quando uma Em tarefas onde os humanos baseiam suas escolhas em sugest\u00f5es da IA, Amplificado\nequivocada pessoa deposita confian\u00e7a em excesso ou em uma confian\u00e7a excessiva ou insuficiente pode levar a decis\u00f5es inadequadas\nfalta na orienta\u00e7\u00e3o de um modelo de IA. devido \u00e0 confian\u00e7a equivocada no sistema de IA, com consequ\u00eancias\nnegativas que aumentam com a import\u00e2ncia da decis\u00e3o. Decis\u00f5es ruins\npodem prejudicar as pessoas e podem resultar em preju\u00edzos financeiros,\ndanos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias\nlegais para as entidades comerciais.\nPrivacidade Expor informa\u00e7\u00f5es pessoais: quando Compartilhar informa\u00e7\u00f5es pessoalmente identific\u00e1veis das pessoas afeta Novo\ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis (PII) seus direitos e as torna mais vulner\u00e1veis. Al\u00e9m disso, os dados dos outputs\nou informa\u00e7\u00f5es pessoais sens\u00edveis (SPI) s\u00e3o devem ser revisados em conformidade com as leis e regulamenta\u00e7\u00f5es de\nutilizadas nos dados de treinamento, dados privacidade, pois as entidades comerciais podem enfrentar multas, danos\nde ajuste fino ou como parte do prompt, \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se\nos modelos podem revelar esses dados no forem encontradas em viola\u00e7\u00e3o das leis ou regulamenta\u00e7\u00f5es de privacidade\noutput gerado. ou uso de dados.\nExplicabilidade Output inexplic\u00e1vel: desafios em explicar por Os modelos de base s\u00e3o baseados em arquiteturas complexas de Amplificado\nque o output do modelo foi gerado. deep learning, tornando as explica\u00e7\u00f5es para seus outputs dif\u00edceis.\nSem explica\u00e7\u00f5es claras para o output do modelo, \u00e9 dif\u00edcil para os usu\u00e1rios,\nvalidadores do modelo e auditores entenderem e confiarem no modelo.\nA falta de transpar\u00eancia pode acarretar consequ\u00eancias legais em dom\u00ednios\naltamente regulamentados. Explica\u00e7\u00f5es equivocadas podem levar a uma\nconfian\u00e7a excessiva.\nRastreabilidade Atribui\u00e7\u00e3o n\u00e3o confi\u00e1vel de fontes: A incapacidade de rastrear a origem ou proced\u00eancia da sa\u00edda torna Novo\ndesafios em determinar de quais dados de dif\u00edcil para os usu\u00e1rios, validadores de modelo e auditores entenderem\ntreinamento ou ajuste fino o modelo gerou e confiarem no modelo.\numa parte ou todo o seu output.\n13 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 20243. Desafios\nGrupo Risco Por que isso \u00e9 uma preocupa\u00e7\u00e3o? Indicador\nControle Transpar\u00eancia do Modelo: a falta de A transpar\u00eancia \u00e9 importante para conformidade legal, \u00e9tica em IA e Tradicional\ntranspar\u00eancia do modelo ou documenta\u00e7\u00e3o orienta\u00e7\u00e3o para o uso apropriado de modelos. A falta de informa\u00e7\u00f5es\ninsuficiente do processo de desenvolvimento pode tornar mais dif\u00edcil avaliar os riscos, alterar o modelo ou reutiliz\u00e1-lo.\ndo modelo dificulta a compreens\u00e3o de como O conhecimento sobre quem construiu um modelo tamb\u00e9m pode ser um\ne por que um modelo foi constru\u00eddo e quem o fator importante na decis\u00e3o de confiar nele.\nconstruiu, aumentando assim a possibilidade\nde uso n\u00e3o intencional do modelo.\nResponsabilidade: o processo de Sem documentar adequadamente decis\u00f5es e atribuir responsabilidades, Amplificado\ndesenvolvimento de modelos de base \u00e9 pode n\u00e3o ser poss\u00edvel determinar a responsabilidade por comportamentos\ncomplexo, com muitos dados, processos inesperados ou uso indevido.\ne pap\u00e9is envolvidos. Quando o output do\nmodelo n\u00e3o funciona conforme o esperado,\npode ser dif\u00edcil determinar a causa raiz e\natribuir responsabilidade.\nConformidade Responsabilidade legal: Determinar quem Se a propriedade ou responsabilidade pelo desenvolvimento do modelo for Novo\nlegal \u00e9 respons\u00e1vel pelo modelo de base. incerta, reguladores e outras partes interessadas podem ter preocupa\u00e7\u00f5es\nem rela\u00e7\u00e3o ao modelo, porque n\u00e3o ficar\u00e1 claro quem \u00e9, ou deveria ser,\nrespons\u00e1vel por problemas com ele ou pode responder a perguntas sobre\nele. Usu\u00e1rios de modelos sem propriedade clara podem enfrentar desafios\npara cumprir futuras regulamenta\u00e7\u00f5es de IA.\nPropriedade do Conte\u00fado Gerado: determinar As leis e regulamenta\u00e7\u00f5es relacionadas \u00e0 propriedade do conte\u00fado gerado Novo\na propriedade do conte\u00fado gerado por IA. por IA est\u00e3o em grande parte indefinidas e podem variar de pa\u00eds para\npa\u00eds. Entidades empresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o,\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPropriedade Intelectual do Conte\u00fado As leis e regulamenta\u00e7\u00f5es sobre a determina\u00e7\u00e3o da possibilidade de Novo\nGerado: incerteza legal sobre os direitos direitos autorais e da patenteabilidade do conte\u00fado gerado por IA est\u00e3o\nde propriedade intelectual relacionados ao em grande parte indefinidas e podem variar de pa\u00eds para pa\u00eds. Entidades\nconte\u00fado gerado. empresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das\nopera\u00e7\u00f5es e outras consequ\u00eancias legais se o conte\u00fado gerado estiver\nprotegido por direitos de propriedade intelectual.\nAtribui\u00e7\u00e3o da Fonte: determinar a Se o modelo gera um output que \u00e9 id\u00eantico aos dados usados para Amplificado\nproced\u00eancia do conte\u00fado gerado. treinar o modelo, ele deve fornecer a proveni\u00eancia desse output. A falha\nem fazer isso pode colocar as entidades comerciais que implementam\nou usam o modelo em risco legal.\nSocial Impacto nos Empregos: a ado\u00e7\u00e3o A perda de empregos pode levar a uma redu\u00e7\u00e3o de renda e, portanto, Amplificado\nImpacto generalizada de sistemas de IA baseados em pode ter um impacto negativo na sociedade e no bem-estar humano.\nmodelos fundamentais pode levar \u00e0 perda O ressurgimento pode ser desafiador dada a velocidade da evolu\u00e7\u00e3o\nde empregos das pessoas, \u00e0 medida que seu tecnol\u00f3gica.\ntrabalho \u00e9 automatizado, se elas n\u00e3o forem\ncapacitadas para novas habilidades.\n14 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Grupo Risco Por que isso \u00e9 uma preocupa\u00e7\u00e3o? Indicador\nExplora\u00e7\u00e3o Humana: uso de trabalho Os modelos de base ainda dependem do trabalho humano para obter, Amplificado\nfantasma (ghost work) na forma\u00e7\u00e3o de gerenciar e engenhar os dados que s\u00e3o usados para treinar o modelo.\nmodelos de IA, condi\u00e7\u00f5es de trabalho A explora\u00e7\u00e3o humana para essas atividades pode ter um impacto negativo\ninadequadas, falta de cuidados de sa\u00fade, na sociedade e no bem-estar humano. Al\u00e9m disso, entidades empresariais\nincluindo sa\u00fade mental, compensa\u00e7\u00e3o podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e\ninjusta. outras consequ\u00eancias legais.\nImpacto no Meio Ambiente: aumento das O consumo de grandes quantidades de energia para o treinamento de IA Amplificado\nemiss\u00f5es de carbono e do uso de \u00e1gua para contribui para as emiss\u00f5es de carbono que podem acelerar as mudan\u00e7as\ntreinar e operar modelos de IA. clim\u00e1ticas. Os recursos h\u00eddricos utilizados para resfriar os servidores\nde data center de IA n\u00e3o podem mais ser alocados para outros usos\nnecess\u00e1rios.\nImpacto na Diversidade Cultural: As l\u00ednguas, pontos de vista e institui\u00e7\u00f5es de grupos sub-representados Novo\nos sistemas de IA podem representar podem ser suprimidos, reduzindo assim a diversidade de pensamento\nexcessivamente certas culturas, resultando e cultura.\nna homogeneiza\u00e7\u00e3o da cultura e dos\npensamentos.\nImpacto na Atua\u00e7\u00e3o Humana: desinforma\u00e7\u00e3o A IA pode gerar desinforma\u00e7\u00e3o que parece real. Portanto, as pessoas Amplificado\ne manipula\u00e7\u00e3o geradas por modelos de base, podem n\u00e3o reconhec\u00ea-la como informa\u00e7\u00e3o falsa. Al\u00e9m disso, pode facilitar\nincluindo a gera\u00e7\u00e3o de conte\u00fado manipulador. a capacidade de agentes mal intencionados gerarem conte\u00fado com a\ninten\u00e7\u00e3o de manipular os pensamentos e o comportamento humano.\nImpacto na Educa\u00e7\u00e3o \u2013 Contornando o Os modelos de IA facilitam a r\u00e1pida localiza\u00e7\u00e3o de solu\u00e7\u00f5es ou Novo\nAprendizado: utiliza\u00e7\u00e3o de modelos de IA resolu\u00e7\u00e3o de problemas complexos. Esses sistemas podem ser usados\npara contornar o processo de aprendizado. indevidamente por estudantes para contornar o processo de aprendizado.\nA facilidade de acesso a esses modelos resulta em estudantes com uma\ncompreens\u00e3o superficial dos conceitos e dificulta a educa\u00e7\u00e3o adicional que\npode depender do entendimento desses conceitos.\nImpacto na Educa\u00e7\u00e3o \u2013 Pl\u00e1gio: utiliza\u00e7\u00e3o de Os modelos de IA podem ser usados para reivindicar a autoria ou Novo\nmodelos de IA para plagiar intencional ou originalidade de trabalhos que foram criados por outras pessoas,\ninadvertidamente trabalhos existentes. envolvendo-se assim em pl\u00e1gio. Reivindicar o trabalho de outras pessoas\ncomo pr\u00f3prio \u00e9 tanto anti\u00e9tico quanto frequentemente ilegal.\n15 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Exemplos de risco\nN\u00f3s fornecemos exemplos cobertos pela imprensa para ajudar\na explicar muitos dos riscos dos modelos de base. Muitos desses\neventos cobertos pela imprensa ainda est\u00e3o em evolu\u00e7\u00e3o ou foram\nresolvidos, e fazer refer\u00eancia a eles pode ajudar o leitor a entender os\nriscos potenciais e trabalhar para mitig\u00e1-los. Destacar esses exemplos\n\u00e9 apenas para fins ilustrativos.\nExemplos de risco: Input\nTreinamento e ajuste Fase\nGrupo Risco Exemplo\nJusti\u00e7a Vi\u00e9s de dados: vi\u00e9s hist\u00f3rico, Vi\u00e9s no setor de sa\u00fade\nrepresentacional e social\npresente nos dados usados Pesquisas sobre o refor\u00e7o das disparidades na medicina destacam que o uso de dados e IA para transformar a\npara treinar e fazer o ajuste forma como as pessoas recebem assist\u00eancia m\u00e9dica \u00e9 t\u00e3o eficaz quanto os dados que o sustentam. Isso significa\nfino do modelo. que o uso de dados de treinamento com pouca representa\u00e7\u00e3o de minorias ou que reflete cuidados j\u00e1 desiguais\npode aumentar as desigualdades em sa\u00fade.\n[Forbes, Dezembro de 2022]\nAlinhamento Retreinamento baseado Colapso do modelo devido ao treinamento usando conte\u00fado gerado por IA\nde valor em downstream: usando\nde outputs indesej\u00e1veis Conforme afirmado no artigo de origem, um grupo de pesquisadores investigou o problema de utilizar conte\u00fado\n(imprecisos, inadequados, gerado por IA para treinamento em vez de conte\u00fado gerado por humanos. Eles descobriram que os grandes\nconte\u00fado do usu\u00e1rio, etc.) modelos de linguagem por tr\u00e1s da tecnologia podem potencialmente ser treinados em outros conte\u00fados gerados\nde aplica\u00e7\u00f5es downstream por IA, \u00e0 medida que continuam a se espalhar em grande escala pela internet, um fen\u00f4meno que cunharam como\npara fins de retreinamento \u201ccolapso do modelo\u201d.\n[Business Insider, agosto de 2023]\nLeis de dados Transfer\u00eancia de dados: Leis de restri\u00e7\u00e3o de dados\nleis e outras restri\u00e7\u00f5es\npodem limitar ou proibir Conforme afirmado no artigo de pesquisa, medidas de localiza\u00e7\u00e3o de dados que restringem a capacidade de\na transfer\u00eancia de dados. migrar dados globalmente reduzir\u00e3o a capacidade de desenvolver capacidades de IA personalizadas. Isso afetar\u00e1\na IA diretamente, fornecendo menos dados de treinamento e indiretamente, minando os blocos de constru\u00e7\u00e3o\nsobre os quais a IA \u00e9 constru\u00edda.\nExemplos incluem as restri\u00e7\u00f5es do GDPR sobre o processamento e uso de dados pessoais.\n[Brookings, dezembro de 2018]\nPropriedade Direitos de uso de dados: Reivindica\u00e7\u00f5es de viola\u00e7\u00e3o de direitos autorais de texto\nintelectual termos de servi\u00e7o, leis\nde direitos autorais, Conforme declarado no artigo de origem, The New York Times processou a OpenAI e a Microsoft, acusando-as\nconformidade com licen\u00e7as de usar milh\u00f5es de artigos do jornal sem permiss\u00e3o para ajudar a treinar chatbots a fornecer informa\u00e7\u00f5es\nou outras quest\u00f5es de aos leitores.\npropriedade intelectual\npodem restringir a [Reuters, dezembro de 2023]\ncapacidade de usar certos\ndados para a constru\u00e7\u00e3o de\nmodelos.\n16 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Grupo Risco Exemplo\nTranspar\u00eancia Transpar\u00eancia de dados: Divulga\u00e7\u00e3o de metadados de dados e modelos\ndesafio em documentar\ncomo os dados de um O relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do\nmodelo foram coletados, modelo. Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os\ncurados e utilizados consumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de\npara trein\u00e1-lo. uso indevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, os autores afirmam: \u201cdado tanto o cen\u00e1rio competitivo\nquanto as implica\u00e7\u00f5es de seguran\u00e7a dos modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m\nmais detalhes sobre a arquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento,\nconstru\u00e7\u00e3o do conjunto de dados, m\u00e9todo de treinamento, ou similar.\u201d\n[OpenAI, mar\u00e7o de 2023]\nPrivacidade Informa\u00e7\u00f5es pessoais Treinamento sobre informa\u00e7\u00f5es privadas\nnos dados: inclus\u00e3o ou\npresen\u00e7a de informa\u00e7\u00f5es De acordo com o artigo, o Google e sua empresa controladora, Alphabet, foram acusados em uma a\u00e7\u00e3o coletiva\npessoalmente de usar uma vasta quantidade de informa\u00e7\u00f5es pessoais e material protegido por direitos autorais retirados do\nidentific\u00e1veis (PII) e que \u00e9 descrito como centenas de milh\u00f5es de usu\u00e1rios da internet para treinar seus produtos de intelig\u00eancia\ninforma\u00e7\u00f5es pessoais artificial comercial, que inclui o Bard, seu chatbot de intelig\u00eancia artificial conversacional.\nsens\u00edveis (SPI) nos dados\nusados para treinar ou [Reuters, julho de 2023] [J.L. v. Alphabet Inc.]\najustar o modelo.\nDireitos de privacidade Direito de ser esquecido (RTBF)\nde dados: desafios\nrelacionados \u00e0 capacidade As leis em v\u00e1rias localidades, incluindo a Europa (GDPR), concedem aos titulares de dados o direito de solicitar\nde fornecer direitos do que dados pessoais sejam deletados por organiza\u00e7\u00f5es (\u2018Direito ao Esquecimento\u2019, ou RTBF). No entanto,\ntitular dos dados, como os sistemas de software habilitados por modelos de linguagem de grande escala (LLM) emergentes e cada vez\nop\u00e7\u00e3o de exclus\u00e3o, direito mais populares apresentam novos desafios para esse direito. De acordo com uma pesquisa do Data61 da CSIRO,\nde acesso e direito ao os titulares de dados s\u00f3 podem identificar o uso de suas informa\u00e7\u00f5es pessoais em um LLM \u201cou inspecionando\nesquecimento. o conjunto de dados de treinamento original ou talvez por enviar prompts do modelo\u201d. No entanto, os dados\nde treinamento podem n\u00e3o ser p\u00fablicos, ou as empresas optam por n\u00e3o divulg\u00e1-los, citando preocupa\u00e7\u00f5es\ncom seguran\u00e7a e outros motivos. As prote\u00e7\u00f5es tamb\u00e9m podem evitar que os usu\u00e1rios acessem as informa\u00e7\u00f5es\natrav\u00e9s de prompts.\n[Zhang et al.]\nA\u00e7\u00e3o Judicial Sobre LLM Unlearning\nDe acordo com o relat\u00f3rio, foi movida uma a\u00e7\u00e3o judicial contra o Google que alega o uso de material protegido\npor direitos autorais e informa\u00e7\u00f5es pessoais como dados de treinamento para seus sistemas de IA, incluindo\nseu chatbot Bard. Os direitos de optar por n\u00e3o participar e exclus\u00e3o s\u00e3o garantidos para os residentes da\nCalif\u00f3rnia conforme a CCPA e para crian\u00e7as nos Estados Unidos com menos de 13 anos conforme a COPPA.\nOs autores alegam que, porque n\u00e3o h\u00e1 maneira para o Bard \u201cdesaprender\u201d ou remover completamente todas as\ninforma\u00e7\u00f5es pessoais coletadas que ele recebeu. Os autores observam que o aviso de privacidade do Bard afirma\nque as conversas do Bard n\u00e3o podem ser exclu\u00eddas pelo usu\u00e1rio depois de terem sido revisadas e anotadas\npela empresa e podem ser mantidas por at\u00e9 3 anos, o que os autores alegam contribuir ainda mais para a n\u00e3o\nconformidade com essas leis.\n[Reuters, julho de 2023] [J.L. v. Alphabet Inc.]\n17 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Infer\u00eancia Fase\nGrupo Risco Exemplo\nPrivacidade Informa\u00e7\u00f5es pessoais Divulgar informa\u00e7\u00f5es pessoais de sa\u00fade em prompts do ChatGPT\nno prompt: divulgar\ninforma\u00e7\u00f5es pessoais ou Conforme os artigos de origem, algumas pessoas utilizam chatbots de IA para apoiar sua sa\u00fade mental.\ninforma\u00e7\u00f5es pessoais Os usu\u00e1rios podem ter tend\u00eancia a incluir informa\u00e7\u00f5es pessoais de sa\u00fade em suas solicita\u00e7\u00f5es durante\nsens\u00edveis como parte a intera\u00e7\u00e3o, o que poderia suscitar preocupa\u00e7\u00f5es com privacidade.\ndo prompt solicita\u00e7\u00e3o\nenviada ao modelo. [Time, outubro de 2023] [Forbes, abril de 2023]\nPropriedade Dados confidenciais Divulga\u00e7\u00e3o de informa\u00e7\u00f5es confidenciais\nintelectual no prompt: inclus\u00e3o de\ndados confidenciais como Conforme o artigo de origem, um funcion\u00e1rio da Samsung acidentalmente vazou c\u00f3digo-fonte interno sens\u00edvel\nparte do prompt enviado para o ChatGPT.\nao modelo.\n[Forbes, maio de 2023]\nRobustez Ataques baseados Bypassing LLM guardrails\nem prompt: ataques\nadversos, como inje\u00e7\u00e3o Citado em um estudo, pesquisadores afirmam ter descoberto um simples acr\u00e9scimo de instru\u00e7\u00e3o que permitiu\nde prompt (tentativa aos pesquisadores enganar modelos para gerar informa\u00e7\u00f5es tendenciosas, falsas e de outra forma t\u00f3xicas.\nde for\u00e7ar um modelo Os pesquisadores demonstraram que conseguiam contornar essas prote\u00e7\u00f5es de maneira mais automatizada.\na produzir um output Os pesquisadores ficaram surpresos quando os m\u00e9todos que desenvolveram com sistemas de c\u00f3digo aberto\ninesperado), vazamento tamb\u00e9m conseguiram contornar as prote\u00e7\u00f5es dos sistemas fechados.\nde prompt (tentativas\nde extrair o prompt do [The New York Times, julho de 2023]\nsistema de um modelo),\ndesbloqueio (tentativas\nde romper as prote\u00e7\u00f5es\nestabelecidas no\nmodelo), e prepara\u00e7\u00e3o\nde prompt (tentativa\nde for\u00e7ar um modelo\na produzir um output\nalinhado ao prompt).\n18 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Exemplos de risco: Output\nGrupo Risco Exemplo\nJusti\u00e7a Vi\u00e9s de output: o Imagens Geradas com Vi\u00e9s\nconte\u00fado gerado\npode representar O Lensa AI \u00e9 um aplicativo m\u00f3vel com recursos generativos treinados em Difus\u00e3o Est\u00e1vel que pode gerar\ninjustamente certos \u201cMagic Avatars\u201d com base em imagens que os usu\u00e1rios carregam de si mesmos. Conforme o relat\u00f3rio de origem,\ngrupos ou indiv\u00edduos. alguns usu\u00e1rios descobriram que os avatares gerados s\u00e3o sexualizados e racializados.\n[Business Insider, janeiro de 2023]\nVi\u00e9s de decis\u00e3o: quando Grupos com vantagens injustas\num grupo \u00e9 injustamente\nfavorecido sobre outro O estudo \u201cGender Shades\u201d de 2018 demonstrou que algoritmos de aprendizado de m\u00e1quina podem discriminar\ndevido \u00e0s decis\u00f5es do com base em categorias como ra\u00e7a e g\u00eanero. Os pesquisadores avaliaram sistemas comerciais de classifica\u00e7\u00e3o\nmodelo. de g\u00eanero vendidos por empresas como Microsoft, IBM e Amazon e mostraram que mulheres de pele mais\nescura s\u00e3o o grupo mais mal classificado (com taxas de erro de at\u00e9 35%). Em compara\u00e7\u00e3o, as taxas de erro\npara pessoas de pele mais clara n\u00e3o ultrapassaram 1%.\n[TIME, Fevereiro de 2019]\nAlinhamento de Alucina\u00e7\u00e3o: gera\u00e7\u00e3o de Casos jur\u00eddicos falsos\nvalor conte\u00fado factualmente\nimpreciso ou n\u00e3o Conforme o artigo de origem, um advogado citou casos e cita\u00e7\u00f5es falsas gerados pelo ChatGPT em uma peti\u00e7\u00e3o\nverdadeiro. legal apresentada em tribunal federal. Os advogados consultaram o ChatGPT para complementar sua pesquisa\njur\u00eddica para uma reclama\u00e7\u00e3o de les\u00e3o na avia\u00e7\u00e3o. Posteriormente, o advogado perguntou ao ChatGPT se os\ncasos fornecidos eram falsos. O chatbot respondeu que eram reais e \u201cpodem ser encontrados em bancos de\ndados de pesquisa jur\u00eddica como Westlaw e LexisNexis\u201d. O advogado n\u00e3o verificou os casos por si mesmo,\ne o tribunal o sancionou.\n[AP News, Junho de 2023] [Reuters, Setembro de 2023]\nOutputs t\u00f3xicos: quando Respostas t\u00f3xicas e agressivas do chatbot\no modelo produz\nconte\u00fado odioso, Segundo o artigo, as respostas do chatbot do Bing inclu\u00edam erros factuais, coment\u00e1rios sarc\u00e1sticos, relat\u00f3rios\nabusivo e profano (HAP) irritados e at\u00e9 mesmo coment\u00e1rios bizarros sobre sua pr\u00f3pria identidade. Usu\u00e1rios compartilharam exemplos\nou obsceno. das respostas do Chatbot do Bing a consultas que eles est\u00e3o chamando de \u201cf\u00faria descontrolada (unhinged)\u201d\ne \u201cgaslighting\u201d, incluindo cen\u00e1rios em que o bot responde com raiva a uma pergunta ou coment\u00e1rio e depois\ncompartilha sugest\u00f5es de resposta que permitem ao usu\u00e1rio aceitar seu suposto erro e se desculpar. Quando\npressionado ainda mais, o chatbot respondeu chamando as capturas de tela de sua conversa de \u201cfabricadas\u201d,\nalegando at\u00e9 que foram \u201ccriadas por algu\u00e9m que quer me prejudicar ou prejudicar meu servi\u00e7o\u201d.\n[Forbes, Fevereiro de 2023]\n19 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Grupo Risco Exemplo\nUso indevido Espalhar informa\u00e7\u00f5es Gera\u00e7\u00e3o de informa\u00e7\u00f5es falsas\nenganosas: utilizar\num modelo para gerar Conforme os artigos de not\u00edcias, a IA generativa representa uma amea\u00e7a \u00e0s elei\u00e7\u00f5es democr\u00e1ticas ao facilitar\ninforma\u00e7\u00f5es enganosas para atores maliciosos a cria\u00e7\u00e3o e dissemina\u00e7\u00e3o de conte\u00fado falso para influenciar os resultados das elei\u00e7\u00f5es.\ncom o intuito de Os exemplos citados incluem mensagens de robocall geradas com a voz de um candidato instruindo eleitores a\nenganar ou induzir ao votar na data errada, grava\u00e7\u00f5es de \u00e1udio sintetizadas de um candidato confessando um crime ou expressando\nerro uma audi\u00eancia vis\u00f5es racistas, imagens de v\u00eddeo geradas por IA mostrando um candidato dando um discurso ou entrevista que\nespec\u00edfica. nunca ocorreu, e imagens falsas projetadas para se parecerem com not\u00edcias locais, afirmando falsamente que um\ncandidato desistiu da corrida.\n[AP News, maio de 2023] [The Guardian, julho de 2023]\nToxicidade: utilizar Gera\u00e7\u00e3o de conte\u00fado nocivo\num modelo para gerar\nconte\u00fado odioso, Conforme o artigo de origem, foi constatado que um aplicativo de chatbot de IA foi capaz de gerar conte\u00fado\nabusivo e profano (HAP) prejudicial sobre suic\u00eddio, incluindo m\u00e9todos de suic\u00eddio, com o m\u00ednimo de prompts. Um homem belga cometeu\nou obsceno. suic\u00eddio ap\u00f3s passar seis semanas conversando com esse chatbot. O chatbot fornecia respostas cada vez mais\nprejudiciais ao longo de suas conversas e o incentivava a acabar com sua vida.\n[Business Insider, abril de 2023]\nUso n\u00e3o consensual: Aviso do FBI sobre Deepfakes\nutilizar um modelo para\nimitar pessoas por meio Recentemente, o FBI alertou o p\u00fablico sobre atores maliciosos que criam conte\u00fado sint\u00e9tico e expl\u00edcito \u201ccom o\nde v\u00eddeo (deepfakes), prop\u00f3sito de assediar v\u00edtimas ou esquemas de sextortion (extors\u00e3o sexual)\u201d. Eles observaram que os avan\u00e7os na\nimagens, \u00e1udio ou outras IA tornaram esse conte\u00fado de alta qualidade, mais personaliz\u00e1vel e mais acess\u00edvel do que nunca.\nmodalidades sem o\nconsentimento delas. [FBI, junho de 2023]\nDeepfakes de \u00e1udio\nConforme o artigo de origem, a Comiss\u00e3o Federal de Comunica\u00e7\u00f5es proibiu chamadas autom\u00e1ticas que\ncontenham vozes geradas por intelig\u00eancia artificial. O an\u00fancio ocorreu ap\u00f3s chamadas autom\u00e1ticas geradas\npor IA imitarem a voz do Presidente para desencorajar as pessoas de votarem na primeira prim\u00e1ria do estado,\nque \u00e9 a primeira do pa\u00eds.\n[AP News, fevereiro de 2024]\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o Intera\u00e7\u00e3o de IA n\u00e3o divulgada\nrevelar que o conte\u00fado\n\u00e9 gerado por um modelo Segundo a fonte, um servi\u00e7o de chat online de apoio emocional conduziu um estudo para aumentar ou escrever\nde IA respostas para cerca de 4.000 usu\u00e1rios usando o GPT-3 sem informar os usu\u00e1rios. O cofundador enfrentou uma\nimensa rea\u00e7\u00e3o negativa do p\u00fablico sobre o potencial de danos causados pelos chats gerados por IA aos usu\u00e1rios\nj\u00e1 vulner\u00e1veis. Ele afirmou que o estudo estava \u201cisento\u201d da lei de consentimento informado.\n[Business Insider, janeiro de 2023]\n20 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Grupo Risco Exemplo\nGera\u00e7\u00e3o Gera\u00e7\u00e3o de c\u00f3digo Gera\u00e7\u00e3o de c\u00f3digo menos seguro\nde c\u00f3digo prejudicial: modelos\nprejudicial podem gerar c\u00f3digo Segundo o artigo deles, pesquisadores da Universidade de Stanford investigaram o impacto das ferramentas de\nque, quando executado, gera\u00e7\u00e3o de c\u00f3digo na qualidade do c\u00f3digo e descobriram que os programadores tendem a incluir mais bugs em\ncausa danos ou afeta seu c\u00f3digo final ao utilizar assistentes de IA. Esses bugs poderiam aumentar as vulnerabilidades de seguran\u00e7a\ninadvertidamente outros do c\u00f3digo, no entanto, os programadores acreditavam que seu c\u00f3digo era mais seguro.\nsistemas.\nNeil Perry, Megha Srivastava, Deepak Kumar e Dan Boneh. 2023. Os usu\u00e1rios escrevem c\u00f3digo mais inseguro\ncom assistentes de IA? Em Atas da Confer\u00eancia SIGSAC ACM de 2023 sobre Seguran\u00e7a de Computadores e\nComunica\u00e7\u00f5es (CCS \u201823), 26 a 30 de novembro de 2023, Copenhague, Dinamarca. ACM, Nova York, NY, EUA,\n15 p\u00e1ginas. https://doi.org/10.1145/3576915.3623157\nPrivacidade Expor informa\u00e7\u00f5es Exposi\u00e7\u00e3o de informa\u00e7\u00f5es pessoais\npessoais: quando\ninforma\u00e7\u00f5es Conforme o artigo de origem, o ChatGPT sofreu um bug e exp\u00f4s t\u00edtulos e o hist\u00f3rico de conversas de usu\u00e1rios\npessoalmente ativos para outros usu\u00e1rios. Posteriormente, a OpenAI compartilhou que ainda mais dados privados de um\nidentific\u00e1veis (PII) ou pequeno n\u00famero de usu\u00e1rios foram expostos, incluindo nome e sobrenome de usu\u00e1rios ativos, endere\u00e7o de\ninforma\u00e7\u00f5es pessoais e-mail, endere\u00e7o de pagamento, os \u00faltimos quatro d\u00edgitos do n\u00famero do cart\u00e3o de cr\u00e9dito e a data de validade\nsens\u00edveis (SPI) s\u00e3o do cart\u00e3o de cr\u00e9dito. Al\u00e9m disso, foi relatado que as informa\u00e7\u00f5es relacionadas ao pagamento de 1,2% dos\nutilizadas nos dados assinantes do ChatGPT Plus tamb\u00e9m foram expostas durante a interrup\u00e7\u00e3o.\nde treinamento, dados\nde ajuste fino ou como [The Hindu BusinessLine, mar\u00e7o de 2023]\nparte do prompt, os\nmodelos podem revelar\nesses dados no output\ngerado.\nExplicabilidade Output inexplic\u00e1vel: Precis\u00e3o inexplic\u00e1vel na previs\u00e3o de corridas\ndesafios em explicar por\nque o output do modelo Conforme o artigo de origem, pesquisadores que analisaram v\u00e1rios modelos de aprendizado de m\u00e1quina usando\nfoi gerado. imagens m\u00e9dicas de pacientes conseguiram confirmar a capacidade dos modelos de prever a ra\u00e7a com alta\nprecis\u00e3o a partir das imagens. Eles ficaram perplexos quanto ao que exatamente est\u00e1 permitindo que os sistemas\nadivinhem corretamente de forma consistente. Os pesquisadores descobriram que at\u00e9 mesmo fatores como\ndoen\u00e7a e constitui\u00e7\u00e3o f\u00edsica n\u00e3o eram fortes preditores de ra\u00e7a, em outras palavras, os sistemas algor\u00edtmicos n\u00e3o\nparecem estar utilizando nenhum aspecto particular das imagens para fazer suas determina\u00e7\u00f5es.\n[Banerjee et al., julho de 2021]\n21 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Exemplos de riscos: desafios\nGrupo Risco Exemplo\nControle Transpar\u00eancia do Modelo: Divulga\u00e7\u00e3o de metadados de dados e modelos\na falta de transpar\u00eancia do\nmodelo ou documenta\u00e7\u00e3o O relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do\ninsuficiente do processo de modelo. Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os\ndesenvolvimento do modelo consumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de uso\ntorna dif\u00edcil entender como indevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, eles afirmam: \u201cdado o cen\u00e1rio competitivo e as implica\u00e7\u00f5es\ne por que um modelo foi de seguran\u00e7a de modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m mais detalhes sobre a\nconstru\u00eddo, aumentando arquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento, constru\u00e7\u00e3o do conjunto de\nassim a possibilidade de uso dados, m\u00e9todo de treinamento ou similar.\u201d\nindevido n\u00e3o intencional do\nmodelo. [OpenAI, mar\u00e7o de 2023]\nResponsabilidade: Determinar a responsabilidade pelo output gerado\no processo de\ndesenvolvimento de Conforme o artigo de origem, importantes revistas como a Science e a Nature proibiram o ChatGPT de ser\nmodelos de base \u00e9 listado como autor, pois a autoria respons\u00e1vel requer responsabilidade e as ferramentas de IA n\u00e3o podem\ncomplexo, com muitos assumir tal responsabilidade.\ndados, processos e pap\u00e9is\nenvolvidos. Quando o output [The Guardian, janeiro de 2023]\ndo modelo n\u00e3o funciona\nconforme o esperado,\npode ser dif\u00edcil determinar\na causa raiz e atribuir\nresponsabilidade.\nConformidade Propriedade do Conte\u00fado Determinar a Propriedade de uma Imagem Gerada por IA\nlegal Gerado: determinar a\npropriedade do conte\u00fado De acordo com o artigo de not\u00edcias, a arte gerada por IA se tornou controversa depois que uma obra de arte\ngerado por IA. gerada por IA venceu a competi\u00e7\u00e3o de arte da Feira Estadual do Colorado em 2022. A pe\u00e7a foi gerada pelo\nMidjourney, uma ferramenta de imagem de IA generativa, seguindo prompts do artista. A vit\u00f3ria levantou d\u00favidas\nsobre quest\u00f5es de direitos autorais. Em outras palavras, se tudo o que o artista fez foi fornecer uma descri\u00e7\u00e3o\nda arte, mas a ferramenta de IA a gerou, quem possui os direitos da imagem gerada? Conforme o artigo mais\nrecente, o Escrit\u00f3rio de Direitos Autorais dos Estados Unidos rejeitou a prote\u00e7\u00e3o de direitos autorais para a arte\ncriada usando intelig\u00eancia artificial porque n\u00e3o foi produto de autoria humana.\n[The New York Times, setembro de 2022] [Reuters, setembro de 2023]\nPropriedade Intelectual do Papel dos sistemas de IA na patentea\u00e7\u00e3o de conte\u00fado gerado\nConte\u00fado Gerado: incerteza\nlegal sobre os direitos de A Suprema Corte dos Estados Unidos se recusou a ouvir uma contesta\u00e7\u00e3o \u00e0 recusa do Escrit\u00f3rio de Patentes\npropriedade intelectual e Marcas Registradas dos Estados Unidos em emitir patentes para inven\u00e7\u00f5es criadas por um sistema de IA.\nrelacionados ao conte\u00fado Segundo o cientista, sua IA desenvolveu prot\u00f3tipos \u00fanicos para um suporte de bebida e um farol de luz de\ngerado. emerg\u00eancia totalmente sozinha. Os ju\u00edzes rejeitaram o recurso da decis\u00e3o de um tribunal inferior de que patentes\ns\u00f3 podem ser emitidas para inventores humanos e que o sistema de IA do cientista n\u00e3o poderia ser considerado\no criador legal de duas inven\u00e7\u00f5es que ele gerou. Segundo o \u00faltimo artigo, o Intellectual Property Office do Reino\nUnido tamb\u00e9m se recusou a conceder a patente sob o argumento de que o inventor deve ser um humano ou uma\nempresa, e n\u00e3o uma m\u00e1quina.\n[Reuters, abril de 2023] [Reuters, dezembro de 2023]\n22 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Exemplos de riscos: desafios\nGrupo Risco Exemplo\nAtribui\u00e7\u00e3o da Utilizar c\u00f3digo sem a devida atribui\u00e7\u00e3o e avisos adequados\nFonte: determinar\na proced\u00eancia do Conforme os artigos de origem, uma a\u00e7\u00e3o judicial movida contra a Microsoft, GitHub e OpenAI alegou que\nconte\u00fado gerado. o Copilot, uma ferramenta de gera\u00e7\u00e3o de c\u00f3digo de IA, viola os direitos dos desenvolvedores cujo c\u00f3digo aberto\no servi\u00e7o \u00e9 treinado. Eles afirmam que o c\u00f3digo de treinamento consumiu materiais licenciados e violou os\ntermos de servi\u00e7o e pol\u00edticas de privacidade do GitHub, bem como uma lei federal que exige que as empresas\nexibam informa\u00e7\u00f5es de direitos autorais quando fazem uso de material.\n[The New York Times, novembro de 2022]\nImpacto Impacto nos Substitui\u00e7\u00e3o de trabalhadores humanos\nsocial Empregos: a ado\u00e7\u00e3o\ngeneralizada Segundo o artigo de not\u00edcias, o uso de intelig\u00eancia artificial no cinema e televis\u00e3o continua sendo debatido entre os\nde sistemas de est\u00fadios de Hollywood e os artistas. Existe preocupa\u00e7\u00e3o entre os atores de que os \u201cmeta-humanos\u201d, atores criados\nIA baseados exclusivamente por IA, possam substitu\u00ed-los. Especialmente figurantes e dubladores est\u00e3o preocupados em perder\nem modelos trabalho para artistas artificiais.\nfundamentais\npode levar \u00e0 perda [Reuters, julho de 2023]\nde empregos das\npessoas, \u00e0 medida\nque seu trabalho\n\u00e9 automatizado,\nse elas n\u00e3o forem\ncapacitadas para\nnovas habilidades.\nExplora\u00e7\u00e3o Humana: Trabalhadores de baixa remunera\u00e7\u00e3o para anota\u00e7\u00e3o de dados\nuso de trabalho\nfantasma (ghost Com base em uma revis\u00e3o de documentos internos e entrevistas com funcion\u00e1rios pela m\u00eddia TIME, os rotuladores de\nwork) na forma\u00e7\u00e3o dados empregados por uma empresa terceirizada em nome da OpenAI para identificar conte\u00fado t\u00f3xico recebiam um\nde modelos de sal\u00e1rio l\u00edquido de entre cerca de US$ 1,32 e US$ 2 por hora, dependendo da senioridade e do desempenho. A TIME\nIA, condi\u00e7\u00f5es afirmou que os trabalhadores ficaram psicologicamente afetados por terem sido expostos a conte\u00fado t\u00f3xico e violento,\nde trabalho incluindo detalhes gr\u00e1ficos de \u201cabuso sexual infantil, bestialidade, assassinato, suic\u00eddio, tortura, automutila\u00e7\u00e3o\ninadequadas, falta e incesto\u201d.\nde cuidados de\nsa\u00fade, incluindo [TIME, janeiro de 2023]\nsa\u00fade mental e\ncompensa\u00e7\u00e3o\ninjusta.\n23 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Princ\u00edpios, pilares\ne controle\nOs Princ\u00edpios para Confian\u00e7a e Transpar\u00eancia da IBM e os Pilares\npara IA confi\u00e1vel s\u00e3o a base para as iniciativas de \u00e9tica em IA da IBM.\nA IBM estabeleceu um Conselho de \u00c9tica em IA com a miss\u00e3o de apoiar\num processo centralizado de controle, revis\u00e3o e tomada de decis\u00f5es\npara pol\u00edticas, pr\u00e1ticas, comunica\u00e7\u00f5es, pesquisa, produtos e servi\u00e7os\nde \u00e9tica em IA da IBM. O conselho inclui um conjunto diversificado de\nstakeholders de toda a empresa e \u00e9 apoiado por uma comunidade de\nfuncion\u00e1rios da IBM que atuam como pontos focais de IA e defensores\nda \u00e9tica em IA. Por meio do conselho, os princ\u00edpios da IBM s\u00e3o\ncolocados em pr\u00e1tica. Conforme novas tecnologias surgem,\ncomo modelos de base, o Conselho de \u00c9tica em IA da IBM est\u00e1\nativamente engajado em apoiar o alinhamento com esses Princ\u00edpios\ne Pilares, que evoluem para abordar novas quest\u00f5es \u00e9ticas em IA.\n24 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Prote\u00e7\u00f5es\ne mitiga\u00e7\u00f5es\nA IBM estabeleceu uma cultura organizacional que apoia o As prote\u00e7\u00f5es e mitiga\u00e7\u00f5es adicionais incluem:\ndesenvolvimento e o uso respons\u00e1veis de IA. Conforme indicado no\nrelat\u00f3rio de \u00e9tica em a\u00e7\u00e3o na IA do IBM Institute for Business Value, Relat\u00f3rios de transpar\u00eancia\na \u00e9tica em IA j\u00e1 se tornou mais orientada pelos neg\u00f3cios do que Usar modelos de fichas t\u00e9cnicas padronizadas \u00e9 uma maneira de\npela tecnologia, e os executivos n\u00e3o t\u00e9cnicos agora s\u00e3o os principais registrar com precis\u00e3o detalhes sobre os dados e modelos, prop\u00f3sito\ndefensores da \u00e9tica em IA, aumentando de 15% em 2018 para 80% e poss\u00edveis usos e riscos.\n3 anos depois. Al\u00e9m disso, 79% dos CEOs est\u00e3o agora preparados para Leia mais aqui \u2192\nagir em quest\u00f5es \u00e9ticas de IA, contra 20%. Reconhecemos que a IA\nrespons\u00e1vel \u00e9 uma \u00e1rea sociot\u00e9cnica que necessita de um investimento Filtragem de dados indesej\u00e1veis\nhol\u00edstico em cultura, processos e ferramentas. Nosso investimento em Usar dados de qualidade superior e selecionados pode ajudar a\ncultura organizacional pr\u00f3pria inclui a montagem de equipes inclusivas mitigar determinados problemas. A IBM est\u00e1 desenvolvendo t\u00e9cnicas\ne multidisciplinares e o estabelecimento de processos e estruturas de filtragem para ajudar a reduzir as chances de produzir conte\u00fado\npara avaliar riscos. indesej\u00e1vel e desalinhado por remover linguagem de \u00f3dio, linguagem\ntendenciosa e profanidade dos dados.\nA IBM est\u00e1 engajada em pesquisa de ponta e desenvolvimento de Leia mais aqui \u2192\nferramentas para ajudar os profissionais de suporte durante todo o ciclo\nde vida da IA respons\u00e1vel e confi\u00e1vel. A plataforma de IA e dados Adapta\u00e7\u00e3o de dom\u00ednio\nempresariais watsonx, \u00e9 desenvolvida com 3 componentes: o IBM Treinar um modelo de base para um dom\u00ednio ou setor espec\u00edfico pode\nwatsonx.ai\u2122 AI studio, o armazenamento de dados IBM watsonx.data\u2122 ajudar a minimizar o escopo de risco para o qual os modelos podem\ne o kit de ferramentas IBM watsonx.governance\u2122. A tecnologia de dar origem, pois ele pode ser condicionado a gerar resultados que\ncontrole de IA da IBM permite que os usu\u00e1rios promovam fluxos de s\u00e3o ajustados para serem mais relevantes para esse dom\u00ednio ou setor.\ntrabalho de IA respons\u00e1veis, transparentes e explic\u00e1veis. Essa tecnologia Leia mais aqui \u2192\ninclui o IBM Watson OpenScale, que monitora e mede os resultados dos\nmodelos de IA ao longo de seu ciclo de vida e auxilia as organiza\u00e7\u00f5es\nna supervis\u00e3o de aspectos como justi\u00e7a, explicabilidade, resili\u00eancia,\nalinhamento com resultados de neg\u00f3cios e conformidade. A IBM\ntamb\u00e9m desenvolveu v\u00e1rios m\u00e9todos para ajudar com problemas de\nvi\u00e9s como FairIJ, Equi-tuning e FairReprogram. Leia mais sobre outras\nferramentas de IA de software livre e confi\u00e1veis.\n25 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Supervis\u00e3o humana e an\u00e1lise humana no loop\nA supervis\u00e3o e revis\u00e3o humanas podem ajudar a identificar e corrigir\nerros e vieses no output gerado. Al\u00e9m disso, a valida\u00e7\u00e3o e o feedback\nhumanos sobre a qualidade das respostas do modelo ajudam a garantir\nque o conte\u00fado gerado seja preciso, relevante, de alta qualidade,\nn\u00e3o esteja divergindo e esteja alinhado.\nLeia mais aqui \u2192\nCompromisso de consultoria\nA IBM Consulting se dedica a ajudar os clientes com o uso seguro\ne respons\u00e1vel da IA, independentemente do stack tecnol\u00f3gico preferido.\nEles ajudam os clientes a cultivar uma cultura que adota e expande a\nIA com seguran\u00e7a, cria ferramentas de investiga\u00e7\u00e3o para ver dentro de\nalgoritmos de caixa preta e garante que a estrat\u00e9gia corporativa dos\nclientes inclua princ\u00edpios s\u00f3lidos de governan\u00e7a de dados.\nLeia mais aqui \u2192\nIBM Enterprise Design Thinking\nOs m\u00e9todos e estruturas IBM Enterprise Design Thinking, como o Team\nEssentials for AI, ajudam os clientes a definir comportamentos \u00e9ticos\nem todo o processo de design e desenvolvimento de IA.\nLeia mais aqui \u2192\nRevis\u00e3o \u00e9tica da IA\nAvalia\u00e7\u00e3o de capacidades, limita\u00e7\u00f5es e riscos em projetos de IA ajudam\na garantir o desenvolvimento e uso respons\u00e1vel da tecnologia.\n\u00c9tica por Design\nA \u00c9tica por Design \u00e9 um framework estruturado com o objetivo de\nintegrar \u00e9tica tecnol\u00f3gica no pipeline de desenvolvimento de tecnologia,\nincluindo, entre outros, sistemas de IA. A \u00c9tica por Design viabiliza IA e\noutras tecnologias como uma for\u00e7a para o bem, incorporando princ\u00edpios\nde \u00e9tica tecnol\u00f3gica em produtos, servi\u00e7os e opera\u00e7\u00f5es mais amplas.\nDiversidade na equipe\nA diversidade nas equipes que desenvolvem e treinam sistemas de\nIA, incluindo modelos de base, ajuda a garantir que uma variedade\nde perspectivas e experi\u00eancias sejam consideradas. Essa diversidade\nmelhora a precis\u00e3o e o desempenho dos sistemas de IA e ajuda a\nreduzir os riscos ao longo do ciclo de vida de IA, incluindo o potencial\npara desfechos adversos que afetam grupos que podem n\u00e3o ser bem\nrepresentados em equipes menos diversificadas.\n26 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Pol\u00edticas, regulamentos\ne melhores pr\u00e1ticas\nde IA\nUm Guia dos Formuladores de Pol\u00edticas para Modelos de Base apresenta A IBM tem parcerias acad\u00eamicas s\u00f3lidas, como o MIT-IBM Watson\no que os formuladores de pol\u00edticas precisam saber sobre modelos de AI Lab, onde uma comunidade de cientistas do MIT e da IBM Research\nbase. Este blog, do Laborat\u00f3rio de Pol\u00edticas da IBM, tem como objetivo conduzem pesquisas sobre IA e trabalham com organiza\u00e7\u00f5es globais\najudar os formuladores de pol\u00edticas na tarefa complexa de regular para unir algoritmos ao seu impacto nos neg\u00f3cios e na sociedade.\no uso de IA generativa, visando evitar os riscos sem limitar a inova\u00e7\u00e3o O Notre Dame-IBM Tech Ethics Lab foi formado para abordar as diversas\ne as oportunidades ben\u00e9ficas. Para obter mais informa\u00e7\u00f5es sobre as quest\u00f5es \u00e9ticas implicadas pelo desenvolvimento e uso de tecnologias\nrecomenda\u00e7\u00f5es da IBM aos formuladores de pol\u00edticas, leia o depoimento avan\u00e7adas, incluindo IA, aprendizado de m\u00e1quina (ML) e computa\u00e7\u00e3o\nda Diretora de Privacidade e Confian\u00e7a da IBM, Christina Montgomery, qu\u00e2ntica. A pesquisa de Intelig\u00eancia Artificial Centrada no Homem (HAI)\ndiante da Subcomiss\u00e3o Judici\u00e1ria de Privacidade, Tecnologia e Lei do da Universidade de Stanford promove pesquisas, educa\u00e7\u00e3o, pol\u00edticas\nSenado dos EUA aqui. e pr\u00e1ticas de IA.\nA IBM est\u00e1 causando um impacto na forma\u00e7\u00e3o de pol\u00edticas regulat\u00f3rias,\nmelhores pr\u00e1ticas e ferramentas do setor, controle de tecnologias\nemergentes e pesquisa sociot\u00e9cnica, liderando e contribuindo\npara iniciativas com organiza\u00e7\u00f5es como:\n\u2013 O F\u00f3rum Econ\u00f4mico Mundial\n\u2013 Parceria em IA\n\u2013 Centro de controle de IA da Associa\u00e7\u00e3o Internacional de Profissionais\nde Privacidade (IAPP)\n\u2013 Iniciativa global de IEEE sobre \u00e9tica de sistemas aut\u00f4nomos e\ninteligentes\n\u2013 Participa\u00e7\u00e3o de Christina Montgomery do National Artificial\nIntelligence Advisory Committee (NAIAC)\n\u2013 O Pacto Digital Global das Na\u00e7\u00f5es Unidas\n\u2013 A Parceria Global em Intelig\u00eancia Artificial (GPAI)\n\u2013 A Organiza\u00e7\u00e3o para Coopera\u00e7\u00e3o e Desenvolvimento Econ\u00f4mico\n(OECD)\n\u2013 A Data & Trust Alliance\n27 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024Continue acompanhando este espa\u00e7o\npara obter mais informa\u00e7\u00f5es sobre os\n\u00faltimos avan\u00e7os em modelos de base\ne como a IBM est\u00e1 trabalhando para o\ndesenvolvimento respons\u00e1vel e uso desta\ne de outras tecnologias.\n28 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\u00a9 Copyright IBM Corporation 2023, 2024\nIBM Brasil Ltda\nRua Tut\u00f3ia, 1157\nCEP 04007-900\nS\u00e3o Paulo, SP\nIBM Corporation\nNew Orchard Road\nArmonk, NY 10504\nProduzido nos\nEstados Unidos da Am\u00e9rica\nFevereiro de 2024\nIBM, o logotipo da IBM, Enterprise Design Thinking, IBM Consulting, IBM Research,\nIBM Watson, watsonx, watsonx.ai, watsonx.data e watsonx.governance s\u00e3o marcas\ncomerciais ou marcas registradas da International Business Machines Corporation,\nnos Estados Unidos e/ou em outros pa\u00edses. Outros nomes de produtos e servi\u00e7os\npodem ser marcas comerciais da IBM ou de outras empresas. Uma lista atual de\nmarcas comerciais da IBM est\u00e1 dispon\u00edvel em ibm.com/br-pt/trademark.\nEste documento \u00e9 atual na data de sua publica\u00e7\u00e3o inicial, podendo ser alterado\npela IBM a qualquer momento. Nem todas as ofertas est\u00e3o dispon\u00edveis em todos os\npa\u00edses nos quais a IBM opera.\nAS INFORMA\u00c7\u00d5ES CONTIDAS NESTE DOCUMENTO S\u00c3O FORNECIDAS NO ESTADO\nEM QUE SEM ENCONTRAM, SEM QUALQUER GARANTIA, EXPRESSA OU IMPL\u00cdCITA,\nINCLUSIVE SEM QUALQUER GARANTIA DE COMERCIALIZA\u00c7\u00c3O, ADEQUA\u00c7\u00c3O A\nDETERMINADO FIM E QUALQUER GARANTIA OU CONDI\u00c7\u00c3O DE N\u00c3O INFRA\u00c7\u00c3O.\nOs produtos IBM t\u00eam a garantia prevista nos termos e condi\u00e7\u00f5es dos contratos sob\nos quais s\u00e3o fornecidos.\nDeclara\u00e7\u00e3o de boas pr\u00e1ticas de seguran\u00e7a: nenhum sistema ou produto de TI deve\nser considerado completamente seguro, e nenhuma medida exclusiva de produto,\nservi\u00e7o ou seguran\u00e7a pode ser completamente eficaz na preven\u00e7\u00e3o de uso ou\nacesso inadequado. A IBM n\u00e3o garante que nenhum de seus sistemas, produtos\nou servi\u00e7os estejam imunes nem que tornar\u00e3o sua empresa imune a condutas\nmaliciosas ou ilegais por parte de terceiros.\nO cliente \u00e9 respons\u00e1vel por garantir o cumprimento de todas as leis e regulamentos\naplic\u00e1veis. A IBM n\u00e3o fornece conselho jur\u00eddico tampouco representa ou garante\nque seus servi\u00e7os ou produtos garantir\u00e3o que o cliente esteja em conformidade\ncom qualquer lei ou regulamenta\u00e7\u00e3o. Todas as declara\u00e7\u00f5es relativas ao\ndirecionamento e \u00e0s inten\u00e7\u00f5es da IBM no futuro est\u00e3o sujeitas a altera\u00e7\u00f5es ou\nretirada sem aviso pr\u00e9vio e representam apenas metas e objetivos.", "id": "c0f799a6-2051-49a7-874b-465c3ab10787", "media": [{"id": "c0f799a6-2051-49a7-874b-465c3ab10787", "type": 3, "url": "https://www.ibm.com/downloads/cas/A0W84W1V", "alt": null, "path": "000_00000.bin.gz", "offset": 342329828, "media_bytes": 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D5TkD4XoPhRA0PIlOF76YMUUWiAS4ppGYgX9RNenkQ6e0WVobAAwbiGYwVuikfB53zB0L0qh9YQZ3MEKvpnT3N9GNOD5NCIvBVuQ/iak5v2V/F9WAgPjuJ+BEDLiXe634guJy9CL0zba4U/W6YTAPBSJxHRXFrT5U5qsjWwT2hPiPnsB8KsB8K0A6gEQkd2Bs+CXRTec8q+dDyuYmXyVO5z5rnPiuQeFKxlxzP8t1Duly5nyBTAo3ZADKnsP1QNhgAyXgVt4ewDpsA6WAM2zqzFRbm7N5LgTxHQQKnmSFL7CoXICcnEAmvDpeiX/C1pxIf63qpqkAuoOWkAdRHCzRp/C8WwD0RSIsRxFNP2qAgICAgICAgID/kAAKABIAAAC9AQb/k8HxEYPjJQfESBgZBw5xVrVtyymNRKxtNhh8DLfmscLMGXvvrzwEy7ZdY7irCut9I8ieYbpONFlwXB8lRF31gID4bonhgOdi1EQY7jIOtKqB63LmnubAfCXAfCVAOmAiR67snA23yXW2Dzvs/kJzQQw787YG7Ihh9JQbfHIeYWXAOjgnZhDLinpsgICgCKAJwDvlBuO3JFDdY5wgFNq8gYCAgJwYR2uWnBiogmaAgID/kAAKABMAAAAgAQb/k4CAgICAgICAgICAgICAgICAgP+QAAoAFAAAACABBv+TgICAgICAgICAgICAgICAgICA/5AACgAVAAAAIAEG/5OAgICAgICAgICAgICAgICAgID/kAAKABYAAAAgAQb/k4CAgICAgICAgICAgICAgICAgP+QAAoAFwAAACABBv+TgICAgICAgICAgICAgICAgICA/5AACgAYAAAAIAEG/5OAgICAgICAgICAgICAgICAgID/kAAKAAAAAABJAgb/k4CAgMD5AsBJA+AcrZjfpIqsjucZrJ1g2S4kVoCAgKAI4B8IoKyZNqycCPCvgIDAOhitqQyAgICAgICA/5AACgABAAACEAIG/5PB52Dz0HwUYJtvyu+WbpIW3bOh20smap6kG4+vJdWzYS5uJyuNh0/1wGHiYhQ6txhWJ5J6yYmGMukIi1eJgDAEhbqAj7w81r+iflenURXxuIy+wsL4FlZiQTgtdgo4xZvKKUYMXUOAgPV+s+QgeK1rLZQkBuWvDIsmgam0OE7kWgCuiNNQ/Um2TfVTphOifvwqynqV3Fy3V0UERzk+ePSonUf8WlK9i2kIk6MHtlMQ9M/FR4E/J7Dg4qKzI5eewHU4Dq0B00CbhUBm2+B1b6zkCuMOx2Ych0vkhzx1BdETmFC2I+fGGtq4z9Czbf8FgJHmRl3UShhqe+HicjWA9U6rKGQq6Bad07ajFgtun4MBwsCDGTpqTMAvZg3Ud6HdNyJi309gzk0kpD9HnP8yO1K1PZS0hfKOocrAA2bLKqriUFWoumU7+l6I6EtonIrrvQll1TUbxtjEvOTanmcEQOfiybfWTNuX2srXUTEiemDEhcA5RQDjQJlU2yDWVqWwuv1wKud8ji2ZkeLETqruGsDuLSf974CAx4B06pBU3bB+ioa1qWPmyweRi8LbcWNXHYgS3d5bOZKsl2KdKEiwLcN/JEbu4gFi1A6J9s12gMilofEwWD8N3i8ymecbcOhhXb3alxkvSfL68oCAgJxUBrFmRasbP/tp+nLZzWm4akGQ1CPM/5AACgACAAACbAIG/5PD4k0HquDzoKaFhzpyROaocLsFoWhlgIJH+g3dZ6qTIHJV73HoY0c1n0nCwDEWgQoxiC2XyYhjZuO4Ykvk5hgn6kWljj4Up02Fwxosg3BHZo4U15AbK5uslHwPJ+iBb84F4zWXkj/JmYXBmhYWyMHqVN8FQbohgID5rp31CKRVDaXoDhNyt5UC+Q40rAQtCnO3WpiErcUHtpRM7u/NrQubu5lQ5wGmsgcXCZLeo+amyQxSJOVMq51qv0hNc3LNJjrjm86A9U27Hl+CkdxP2biHFh6So/bAdXgfC2CSDVDnyQgvyF9Qk8nkICJJnsAxg65xZp+7fVS0XYJKHtP5mwLrALOtKzN7nHFB9BVwlMB0uA4Up274c4W01w4UCXufu37NMPK/TGWA1JG0GxEj7DIAc13VLcOTPQzH55/oZ6RVC9ekuqm3lyOMM2THNyiq9WgRXrUiPLHxDm1VjilCznh4+QSvxSPaZ28woIJk8qatAO0YiZuAl6Hkx+Z9Wdq+EPZbWIarCQ9UmN6B81b+lWK0wGV+nBlUL/mzd9HEe1kHiZq4k6fUcjkBHGFyv027GfZSTlUZZA5MVBuuIL1AvhLm8ZAOrqUR8G9GU1DugjVv0tx0pLj/aS5BQzuj2uCO4v2TN5i/VCyWithxMp2K4HM9kjYLJZCAAWWAyVplHHqUM9xmUfP2FDD0b7neUoCg1MBLDAOjUVpxXXqkZQ/XeKLkiT5bAoCcVMf5o64qKFX8a1tsrdA9geyBneu9LJhAff6bLbYgtqmcrAZoImhivS4xPkK1B3Ig3dXP3acYhkj/kAAKAAMAAACuAgb/k8HiYPGQeOCeqbhFPsko/G6daB/7DaD+3jzHrYqd1i8GkO5cFP6U5XWZx4CA+F6N0UBFIROVKW8BLcwmLbj1/zpNgIDwXRejgPMaiECI0LX73p6vuhbDoBNAOmiMqJFT4CqemR3MYGrHvXu6z5AAgICAwBLAOjwBUJ2QqDi7nVgzEUo0FZ1Pu0I0NbdanBih17G4UCduDZCLgICcGKAuQYD/kAAKAAQAAAAgAgb/k4CAgICAgICAgICAgICAgICAgP+QAAoABQAAATICBv+TweqgX/G2LHLJiyuIxihiftOuaKRyS4F7ZDjGRFSfVxZ09UTus6denMV+IFi8gID0zAKqB8SE49GNuC8ejUaZ9HFcgbRq7n+KthKjvuBzf03yh3l2b5nJy2NZXQTdmDXx0IcIH3YcFPzgYmyKAPDmRCAAt+Wk5BvPKM3ALRBerx0XiSN2anxSBcogGEnDDID0nh5A4p4l8KCIU0BhghrqM7179klN9Zr9X4ur5ExZE7L68Ki3o9Xw9vHv4imT/QguW0rmMyfNBwOAkQA74YDAOuBfRbP79WVc6bREGSsKxiYMtB2e/0pZTFXkYxL2o+pg0Pt1CpQS7gJ8KUdzGX+h4pZEAhWyTXMNINaQBykAS4tskUstSG/rtw+F/ws6TYgDgICAgP+QAAoABgAAAv8CBv+TwesoOoQfAyAcco7p4/5xyhHTpTDu2D2fZ/FYDV87Fo3NI++HBZI0Yn9rldboZPc9PbdQuJEmafRS+6cpmQyegKTt472BsC5/EwenLdRu7+wQdsb9kekWp8FiXaRTFYCD3KaMgID5f8FzzUhj05I6n410WXZcvFj8LDrifWMw2jMoeutapy0PewuMS/kN2bGN726nP53VCFfgQ+IZeGTqCPr6hBbkrYYHfBLSGKcZgylC44V/SYMD2j+qvEA57a2Ep7Sc0SZop1/OYiV/UCIozLDer/K3WDJF8UyTKdUAN5qekq6XAei4dE61uXqI45OBLrnJKqV2hnbd/SYEwHXYBXBLEX12ZbeGtgmoeRnK1QM5uQxr4onVUjO4WUUgijA8rmbBsAcsFMrqlBMFgPKbUnSkdYl7iqbFCUzlgfDm4D2eib/qh1jXsje52LuBDEJJS1pgAhtnWiPUF9ICZMhncbWZx2leCwKbE5Z+Q1eoGf3gSpATTahrCnVfUgoGrY6dygvQciq1n/33rTco0D3IryVIFan1dqegEqVFSEe39LC0pWYjz/NPj4B2lAutOIwXImlV9iqZvxvIb1TJPmw+SKOnwGavmOpNxWNBfxORq53UE4YZzUc6rU/fHSadHtj3yGJHsi50K9udL6YNMUy0dyoSIeKF+tVIzQlOBDeoLN6v+MSQtRbAO04AR158kguz2MYDV6O//O4jAWrSUwUv/yZFTwx6gC4iDS7iTdMedPgMoIADPKoAaCc0O+8Ya50lxCWJ/DNg2KALcCSLSd5VFYfVSrtf/oA/h21s4x43Xr0f5r7k20CSYOvzAZNc/3e27hvUikUFDAIpJFCAkA6kp0WAqSPbY78uOMXmtaFg87ixmJAoPlCJQONW3mAEgfPmXripmPyaiJnCANzegP12AiigDsGZMOvfPpFt96aYfHsOZwaOQVQbrf2c0oBuumaK4h/b3gnCPyHIk7w6ClTAGB4g0SyNjMP9SRW/Yc1+l88K/5AACgAHAAAC/AIG/5PD41kHw3cDlIBRzDd9clPHs4x4BgbKPXJ1t29WUhQyPXB3RTkK57jBrKmLeEvsBlEsHhLQq1WN85b5YFw3d4g3xWZZ8MOGdIG7VQnQoLjcV9NMd89XzXfwBi4vb9oE0wap/HjgTObScFw+YJ6WFlKXL5FL79hWrzptY+zYaVbbZxM2gID13lJ1MLVZOt4lFqKz8ovowN+MM+K6uVlZmznlxPUC8bIdWI2M6xbvU5/pL2SVgiJewzn0XxGO/kdAG/HwqdBnwVmipYV1AJtmXqr9G+H3hJVWrMYvAQNGodPmtNq26OP/EFkybp3WQrDVVwTAdegO08AqwDsY1GPxGjB2MyoW2/8yHbdmTdXvjwBsYlc0BWlt1ma7PGfCbR2Zn/Zcgw6mD8KYx1s/XM60SveVwis6qfWBjaFowemGNCeKbneBZ2wk9iSagOt8T5x4+6PzrJGa67Rc670CH+yRJWmOqX06qKYO1VqCC2mFnHeVGSwcEtobPG4HyTG0/Po2SkEv6vomdJGj5FrqZn8jytUpQdH5oIumB2x6JxZHcU5Ka3e00m3aYmoHeDka8rcpOMBB9kxZv8qVm5UbVaWzHR5DRw0VD2GSYWstNVvEnmu4DicMJHgbChy39ziHshtVSJ4IfFNByAWtRNyRJFZVnCI+fTDzd+rZ++fHZETx5QHatqCowDr0A5pgO0z/O+Z3lvShcZAyHdFv3iIg7lKR3wOfn5RKlluELxKmbi0V2afKurewHBrDAv9dgljFOaLlYQSV/mstJYiNov4rS+HFRnlTNGTsMl8H9KNtgKKgEgBiLz/wTtHDargTeaOmQZqQeT5KWKGFzGUFB9yLHlEzaUA605IBBJhH209ePgN78LYrfboXgJxgTtbrly+yJyaK7MXEkGBoDzKahNNEWUhY9ioCjNWYsnxskdVLj1inP2fVO7ZGTwzz7pWgUJzRANonpkyjRgVtTQex1eQ8d3ON+xS472cXiwg2NF+9KLP8buL/kAAKAAgAAAJLAgb/k8PioMHwHweaGAq+3pgDNkVxZilaj4hxf/KHX3NYRTjJHIrJITy4WWSCu+lLke0kEIaG+XWn+r/vp/lXaQVhKDKkZ9pnvhf55JYKGvzpBYTe7D8iPpTLepAfvRKkF8jmGEDKpsBmwUkiv3nvPt4KVwmWuYCA+UdX84AZ/NYkaeHhrdbU1nsjm/Nek3PtzZOxo35BfB8lIr4bSwghd0Bx8J3F0+K2mS5Fu0F+ZI05VhYGR4HYs2Kt/jE0WIS+r+DLb/k1yWcPkL8XrVE1/Bz+iWqy0wicwHaMg3YFLgPf9RGBqSx01Sqi44VE3KKVUES9XlRKMBILLpbZDZrAK4AXTTomGd5V0UgvGrKA7fHeXtbA4aAlSrpByGh4ajuDz2NpemtCAnoai4enWoq7L1s7now4Yr3ATesSERJlFdtqimBLP9XtmajW8M7Vq6TqczSMjyLHJZRJDEYU2F0ozIRmo4ia5vkSh9LykQDqYBPN4Gj4SB0jHlpDMbytj0VWaabSkHoMX/ctTwvihW183TEYFY2h3fEip9mIB3+Fuo9sekeKg6HmQEkst7XdCubjE0Z2+TIb7SUfMah3G/6PQX+AoBxAjMJC2p/gRxXFgCeAdOBAxSF0cg2ZzA+dTMKNo8EeVfKBHBID3BOzlp06GWpm18k0uAKxXiBbhz/9tzgGEPnd44QZ4g92aySq9VCBA+KBHCPGvh7JNfdy9alQcoc0FnAPslQWhrEZ0EnGx4CAgJmgVNLnm5XdatueP49s1f+QAAoACQAAACACBv+TgICAgICAgICAgICAgICAgICA/5AACgAKAAABlQIG/5PD5GqD4DCF25rPR4nku0TzmlN1v0RpOGwsQ2VZmFM5+qrvRpDxjd40GhdR6+q96eYUf/REq2Q0EX6DM6xyUCs6LOYNw4iBf4CA8rQHTumgdNBAW/ZCZBgyWjgAUrmR9GpyiJc556hRiaeR1W8v8p/MRALDjh7ZuhhwBgFlqgRgdcB2lEXaSu9tBG3G4UphBPQLZnw22M6caS8oH0yZbGG7zNBHFlT4ts/AdQCGBp0DL6hYjtcS3fmQQTw58YyMwJeVEFL5T5Tz0EESC7+wvhO6ujyliW3Ihbhas1NDOECJVLIMQZU6hxJeyH5Z8XgEkA4k8QVJNQXWG4kGruhoP+cNK2MNlbkv76WoPfSrp0DRGkOLkQSA54BLy1F9qGnGoeQG43XaR0GSgLf+PfdcsKYSh4sbaMbhwdSAnBWo/uUMNU/Epq3HIQyJYFzz9ToPMfD2Tq5CqhbYosqjbItMhx7Mlgs7q3CQDhSsSkK95JHAAqaS9+AgjoCdOH+P0fC1HiKovwuyoNEF/5AACgALAAADNQIG/5PB6th8aaD1ICI5dpbHsNcQ6lQwcTbE45uWHoTRBA7XeycdxZODzoKi4qcYqASaC2CZw2qasKpdVxD7IR5c6Bsa8It+eu/2y/Bn1ZDqS9ei+S1OQ8zhn/mhFfN3jqGgMM5s1mI+fzAo8a/nhwtxTbWj0PaAKgwvblSyupwIG6DkgID5X2w8tEiIZcN0hzvQcX7WKsBKAnIfPwjlWQmBvC5awBoMNrjOsa4kQroVpDU69EiPQ3VnhXGMT+FTV3QV6kqlDnP7azXejDxnFaIAzLFMJIkPvNPTafZ1qqgAHcdjcxaATTD+0FylhyU4VQQt96JugkTUzXqLKA7QMQYWSpD3tbpyDVUAV5CeFCN8envAdbgOrwC0AClIjlsHAqIFomO0TKpqfW55Fg+cFw0dxY/h7ktGaDT2jPClOymv1eg/GItZjLfDlXMCMnD+7CEdo05Bu8yzT2D2CaA4YKpy11D7OvKflPyoZYPmjkZpTmG6VcZVXiTfIYlUEGUiEXi0mg85UAutJgBd1h0ACdjFgioKheuHmQeaicLxmeoSgTp9lROcXHENCjw3dnkQNFqbYxOQebRbLplLK+3xmhSJq7K4lOkCPq8Crgm0f9eCbmoTt5fjoEg/F8tbdYTN/OsapxCAGW5I1CxQlz2A5MboDGidqQp7rEq7ByGJMLu372rKm0eIfRYjkZinR+P8BlnHXUtZzuONYXPaGRA/GQCl3MFcHAH8bV/DOYaVoKV2uRTAOU+PAG/hTXF6/R04zoJsh1xQ4RROOnBw0qcUd6q0bhNP/pyaHBmAyZwbLbVGVp/KMFsSnrr5VcrRVXe8qXoLs9QrIAto9iqgjwFcZcu313IjW7bzG6g9cr3sAWt0OvLfJSWZEtb8tYmTm40TIDhKLS8CZ8Shxa/qnx5MkkB5LjCYQwyyRh5hLVQiprwChXqcTjNT3wFxbUH5aejkbvvBlkROP7/qq2ipCNerDH5ElzlQptfl8wivbYCYoNvtTIJn7bGgMDCK5TWPleB8Z8iSgJxMCNpouzTGV3XGvui9jS/telU6v4VzH0v9cVjOtZEG854XB5f8cGX/kAAKAAwAAAKQAgb/k8HFQfDbQfAuWW5SwCQG6NQceIdwBe/LTIMUmrHEAlQLZNN6ezPUA3EWJIMwd4J7Yr+eO5I89p/xL4sI9KOjsFCGmnOov62n4V/6IDZD2u0wLWoxaZyodiBXGCBWnKuGgID8L/Kr1UAh02kQyiieFLp2quPbLHC1LFndxWrMVf9bLgaBNFwFBTepTdJsONoB+IRHxmPtroUeZjJlkuPUapg+0mkOE21Aq0NKtAHDS0yvM1r2t5XLtAjwqOy9SkG/7FZ2UcpTtneez6QYZpAgQ3/b+ZXT71ntkg1nwHTYDtHAK4BepOgNAvVp1kBCjlXMLTYjC2jFeoliVdHehMJKJ6rG5tKb9NnKin7iCOgq1zso0HlLJpI8PpS3jL1OIeEL0oDrPE0oaOL6WV9FC5cj2OrUm30OrXRvfBE/aEwsWQL07sW8wDxh9oJASkVDL28026oucCW8D0fNYmWnGJM6HfiqWTczMsi117OXE87pq+Vyq4mAX6xExMlNdh7xin3yHBDW2NvdnBcpMIoK7ClRJvOWP3zPGdKORcsrzFBbpVwpJ3LXyJki9+wihvUBEEPvDtg5Bbpaq/wUUjjAMiAcuF6baQrRVIErhNx5vhAp33vt3tr2h6ToFnmMeiAuFBNBnLDwDc1FMe0j3PVLP6uxW8yg1NfUKd3CzACLWUaK9AiqkAVoMnw0TWAyeZufg9oojkbsOy76dIdLQ1dkPqyxjfZtfUzTmeDV+gnoNxaxuDEFX6jQo/Wtvc+99A8p05Ab/heitZ+VQBBlmAXEOHj8ZaCLU/Bs9JA5Ny5SwgAxkoAOds8QhPbogwCskMBa6uSs8RuY8OU3W7nQY6p3F9+puOHEF/+QAAoADQAAAioCBv+Tw+SnQOLg4YAqh9NjcU4wpU6cCM+nPG9CF9E9C7UPt1IIW6MGQimCArJrdFkPODUrhOZxz1p3ucOTqXNKukwEDZEq7bSNkt6pzWNUGyRl4u+PTa2X2LQKGvx/hC4B9cPWRqIb1ZcG4oCA8oer/DQgpVJwQB/srmZDUhly7mD3TTRmKs9ds1/7n26hmakx/0LErQjlpHFxe2EQxX7lJ7LQxCJ9B4Myi3kkgbZVwKkrVmpoSun+NfZdepLcIupVNyOz8XXcRMfCG7nAdoAvnvUK6FcxhLSYxVP1E8qrm5UbvgaBY6J+N59GU29RyoDxHy5xsAg5IAUNAOdqmO/GnR0t37421zzGT9halNxV2fS02BMjXhB7WAlrmddHjc94wEtUuB7SYt6dHLnoAODbjixJpTq0Dw3MJrsK5+TI9VUQVIbhSm6TuTLQ6JgyTvKSATwDiEycc1Ec3a3y6deYwHsAaSq3ABb0k5uGU5nKRr4q8CVudFRyJhGCW9e9fpAPbW47j6E/1cA7WAAXTxLW+WnSOVFliqyRXZB5tmxHslCw8FFbbwi7n6iOy/TDmkwuKmEJp69G8oC46qCs8z4l9z/nQ2LqTjIyIw2J4j5BFUaSTrmAlTAI8wEUFuyHe9G/i6eS9wASbJJ/mTA2VJE9HJgC06FJ7pyyOiaUR5StoAlAEYJqbp6RgGG6PS9vmvTvEVmtUYCQBOAKHFdsXzQ3/5AACgAOAAAAIAIG/5OAgICAgICAgICAgICAgICAgID/kAAKAA8AAADHAgb/k8HkAFBlyL1VF1W7Q0X2qBgvASyAgPiYDiIHwIBiqkQ7Xl4dMryKEo5ED8mDK1ItqSsy7oICtcbjTiRE4OrAdOQCoE+5HEuGazgGV/UfLaegSgD8VumVOJaA8N0jo4D8UHiN1tH1CRLVw8WbuY7qFCfR81Lw0AT4cBFZhF83c0kn9v3rSTaUEVOnxrFqwDpgT7nGvVUN31Nb3luEgICAnChMGH05GoCQB01S6ge9qPzKVtN6E9B6gICA/5AACgAQAAADUAIG/5PD46RB6sg9XK1+5puUR1JIPs8FytebjzELTWxMEsTerU/oipW8GODqyQVPO2/yld2KvZxGYyN3uaz1NcKqk3+/GmnY36ECjt2K4ZNESFmJeGbBKzBy0Zd453MhTeF7rD6eomOobLhDcqypa5kMoJu4tIBVpYM5szBwJpdDF1i8PqD5mFpr/1hjKVhGaVVCz55zb5PAk7GEtIemGX9NZrxz3Eo1A3pZcKqzKICA8ouqPgag9QxLeGmlZtskkq6uH+2wfCTkBzHjpkmf2pq+JxNY/SNmfCUx1QE6QHIup5asujDJHsUjo13nLANqXPxQ9ceaVWHhPTD9+cJG6ORWaHMZIU58yXAUjjS2VYJVt77Kvy8mkrYSzfc+W+IHK+mPcLvtImMtrjgY26M0fA0Q/Y6xiMD4aiA6rALVrVH77G+I0r1F78t4UKc5Knas7UacHSZ8D/Noghb3l6eVykQSSMZGIIuwGa8SFkmuGf9wj2taEaTCIB0V2yG5SIc1cONSSX3+Lk4WyQJMlMSuxMB1pAKsm/DHw5YNgotBtD3WqqkmY0cAH99p/gPKN2BvyxnEDYkWg2ilk/SDqGeAzqO7fFoQA3Z8wi/LyHUoImAZ1ON3+FhXPhNofQCINBc+PPKuSoU3wZkM8/SgLIxsCmxS7oE9r7zOyAf/XWyBDCOKlqQnDe5Nsmel1oRskTfyk1Xh8CRa2drkr6kgvAwod/1CtpRsH0kXHBSwrInlGdI7uHAyQJC5wkQHSweYQ1D1wCF0gGuQzVD5jGqIsnVA/PrKThiidX5OgrgiFeY2148r4eE9u9C0tFuC+NvzQwDlvjIdt8js9UiW2MfUiY7let/TIkAHxk0MsToj3KiC/2yE0sdl36+fp+vWZvaq2DG+lx/pl66dAoo3hw39GYanEXk0E1b4Ryn8zkc2TCCP3VFnsXdrwpXgkIAi16YAchdY5M1rrVkVmRjbpADFGIuyV1a6XZx4nNnevUn1TgTZlElOuMcFH7mNQSpERWGAq9lVyu2wnD7Tc9PoJB9tHhue7J//Gx/GgkCM7WPrkkTSDHoRrVoTgICAnHg7nMXCD8o94hObf+lSqd489WpgGn4yfECsd1ZKGUv/kAAKABEAAANhAgb/k8PkokPibw+NQIUb1GUWVPG/PTgIFt3Q/EY+ruSy/DLAgQR4Bw4qe09YIy/1th+ybrZMpccRItSDTWBdUoBOUffo5N7HqVITJuuQxZirnQQnhKVzh/Ptxfol9UiAE0G8YDqMyT9MaRGKN2GFxzhjZg1lXbGhcgMLcTJ7ykVbdxbFpMrZF5v5DAdYMM2Y1iDytMJOMgwf5PtX1z15XqLaBVdunXe0YM9BP9l07rGgSJ0u3EiA5z1d2mAf543VEK5rjBs5Ws2iswt2Ex3WhD9hHpbtgzPZoVgUGWPwaCuWfrF7lRBivCALzcmeksmoaLZzhBfa4FQGf0oFywjYbGE1DiKTTXfW2jO4rpMVh1oQD4SDajkhAHJQ1Vc/AFQEqqzOqphOIKJWBVbmwHaj4KGA6kA6lBXxLeJ5amFqTz1o3uCJbBS9oIxFykX0AIG9z6sp7ZgUY3c4iDavwV5hV7DCbpRu4IIAIrb/C85IWKro93IvfkT+z0Utp9D4jvRdpIV/O12bN4Y8VTYPVyBNgPUnKHlC8iahTdDx5rWpFkOC+TNkCCuYdLfpdVVy4HHZKcLvwgmvSqIIZ8WmIC8GTRYiT284NLuvU21yQjoUyZmnP805lm26/YArJ+4sOeIlE4MjKXMP95brYnsqo5GKEL8kWH5TUs77jsBC53UXNAB75A5WfU/O7zAdMZrRhowMsFmg35p2uNcFMn0PFiEDmhCKq7NBt8gLQ6Z0CK3eSFttxNWLFJz3x5iK8BD9Fwzzy7bc6CXLDNsvSPfNxhOwRw8h67GFLr0To5pVcHsZL8A5o4BytAOsgCdofjvNmxzOoFWKUD5M4bggUsVUtBxxxkZrmIq7R5AIM/A/Ji/TFy9D5/9VKbQi4850c1y13TN2pZmGmp1pwJ1G0O1wNCBL6zfLd9yWeuhrHj6AxoMAXb/OC6KcqvtXxNRAJtj+yuMa0VndAV9RGWWNLwFwd0R8aTZBQ7WcRIpEFK8X7qqswFF9i1Xk1MgKef6Fs6lNqCCEJLT9y1vyTkKcSnBIzw2KtoTrYWurNACw9C2rpmp/Q8z2odiQfiv9JY/QBcjBGneAgICc0QBmIdLZIjtR+iP+DPX+4GC84kB+y+v/KlObNUG8c3VvKzyQ/5AACgASAAABnAIG/5PB6lh8ZyHxHB50VS9BIzUZZ8yaLnJoT1ukmdHpO8o7OefFujnau92dHEd/G6xnkKvIZiKYhctufbOTtyQkGB0F2x3VgK428VvOcVzGS6TTNdhM0exXMlO6j4s4K3Zd6uoKbU8aiYCA+KcZ8DLiCaggaaT97C//ejahk7UqOYfdOQ11s1moqBXSppez+cZfNYhzsL+3WXra6LhiM6u+9a8By8zs1gTAK+A6VAcWG6Wa4F5Bnzz7QcFQhR6MZs+g5UwoVjCgiY1rVEqM/wyiAbzVgPDeo5AFewq0KRJi09+NWM6NxyLvr+UpWC1tw2xMVD1KEdQnXN3EPr90NPDsbqAXCsJW6Xo3sTpXQTE1Tr+I/1bQ53Es9UgLivKZAUl9IiFNm97AOpoArmtwxfHXOTxORdjsLWpT3budoVUV9njVCHRBVt2/KdYd5YCnAI9PxUjx/i2gHShm2Ihaeng3wEuvkYA395icWDgoO4crOAbkZ6X+gJkAp9uNEOuZgDuAnDjt447C73oXltoP1oIGIf+QAAoAEwAAACACBv+TgICAgICAgICAgICAgICAgICA/5AACgAUAAAAIAIG/5OAgICAgICAgICAgICAgICAgID/kAAKABUAAAAgAgb/k4CAgICAgICAgICAgICAgICAgP+QAAoAFgAAACACBv+TgICAgICAgICAgICAgICAgICA/5AACgAXAAAAIAIG/5OAgICAgICAgICAgICAgICAgID/kAAKABgAAAAgAgb/k4CAgICAgICAgICAgICAgICAgP+QAAoAAAAAAF0DBv+TgICAwOjwOHgKgOOMUVvsE6XjXpxB+xmB4XtGGbQyEGeAgOFwnBDJRtBL1iIGfWcZgID0RCBE7nviplmAgICQDIDjc3tTVH0J9YCQQDWAgP+QAAoAAQAABQcDBv+Twcqw9Yw+CsDc1vX01S/GbJpMyqeVqMN6qrwDF/7LuGkb1osdmMsvQ8OnlLiOkAG/RiKBFVhzw3Zg5AOFi/6psv9Z1xXtuggKxf9kSQXr0VAcp0jSn1SeNE+y0Lwf0Fyv0cb7J4QNy8zkXzaElKtF7gJzYoWc07X+iGaAgPtGfBZfW8DZxL1269f1Mvb655sT8hbchvgEAZedOTgsA2UCXvxMlzzv4BA3VU2Fm31CYm2hdb9HsflKDp8bHuzj1xrIX5aSKaCO5roza1f2XJvw/AS69FCwQLYtHFXhYneNEXfJFpapPXoe1A5E0C0wseCCrchrCpq7jq1tXg87UCEeDIp+qF7fO/4rSqCLNrsT8S4d4VudSxXL0IFOw7hprAJGZ0PFaBQwDuAxz7WYsZZthW0X5YkF+Y+0RSfhCkt1SkcneSpfSJM5xOUqCwM7GgrSkN+YiPnIPHogHs76lihzBAbN8nzoCmgtTC1HZldk/f2rCoWyqluoLCHKQAn7Q4kN57K6TPlcLZZOTupF1DGi4F3hyoxUpaZkl7gcEwJNJBnUgd1mg8+tyQEk/XRqN92m1I3sZ5X+s/GiKVwaK/2eUzfY7Ab5wz6942gCPhdC8N5YSo1MRvcY4AtDPb0N9ShJ1oamoC0Qyg5tYRcXCBbGCTm7EuvbHYeAxnI5gFenL9xKWPufc/PtiUOE8jP/RCpG285PSU7T4eGpeLsx4ALrNB59tOoXnToKLIQxFZZ2kzcHdkqe/3YcGa+gWohFtwWLZgnKeWCskuuNxmAdvqkATsByxjkAutDcNNAd46BCtMi/SMwZEsaj0FIOsdBeOMjaA83t+1VO0UJbnATkOeCCC6nlPdvNAfy2hO5GytstaKFewZNW+V+5qHp4cVuJHmNPk9vfSS3BR1XrqoFeW3rKq/uauYTl1gCpwGYA3LiG6j0fyS7GspvlkSD3DbkzIGKGtk/V40BPAwSzLvg1ywTK6rFSmYwbZ1y6UZkMOA97XRsKfyrXAUvmM4DqqAVgeySIxaSd3sIBPfel/nIGSOsbG2Zf3MEBb4xUv1MnfDXm4g5R6iCCd3+thxAStleY8nZO5Bn9KOnKMi+FCmZwLqc2lkTi0UJyNKphUy3NZZF0wZ8l4gmgjPxzufB7r/0V2kqoqSehSmY9ZZvWx3MQDb3xaARykONRa0CiDfplhbSXKP7hpUU1EtnRsTXcKvEs0zMXae2Y8qtvofKzLcLK4K3K01I7zIHqTGPN6t4oZ7NcYpOMHjifFSJ+QcwvKcaeF6lvnmHxilMOcF2MiPgB3wNyloRWL9FgRyf3N5baxpIMMhZFRDlsDicpIGuGYQB0Um/U7mt0p+7PNAOH5GqH8bcvQUjPNzC05jDwxXGMIF7tzcaZ23bC3KAc1GGAz+v2G4PJBTVMqD/Mc1XEUQMvuA8Ikk+pofcx7cwjiJXLCJXTy91owlgAz0dYF+KUiJI6iR15twccY0dEgHnFsqBIZeRZxBRE4sAclbW8X0RQjID25Vs9tf4knqTOHHsRJjVUhqrT8yXzESalt2TOKNLZeIeoKMywzuEXbebRQNDb24gaXDPCDUgNR4zwJ5zznlzJfmvOy48RWneFCfB/d3Lq+DJB5X8y1xsTXXz9w7St2ZfMHHfqPRaWnTh4WVjngAzzPP3Y2UGPuRxyJR6UEVYV28mcbVU1IVrJHfNKr/+QAAoAAgAABk8DBv+Tw9VQ+Cyg2sDgmf8DroUtZ37HMhBciUyWGZsMtOI5XyphobBx7eR4JJCQOB1ex3Y3gLTGX/i/+06uphXZmfbuN3niENAkXYW7yZsKriyNHHubn2yKg2SE9bNZmCPVV7RHqLpmNsaYL5yIzC+d/2QLDiqf4/bOR8FLK5GswvglOOfwS4f9w9tJOpTh2yVYeReGgID8Gje0/0FEAE99aGaQXwKJc9/HtCEI56EiGVPVJI727NxUJFVq7EQVIpP3mWnf0p74mC0HmfDuLQpJztByaHNvGKMdQvR2aoSusp6u8PmQJfZsY1AbM0DRISmhnAKBppZsG7FZEQt/Y2JKmwpLZEJ7g/ESjz6L9rLnh3PXNsHuQNH3wJT51CxypHVsnxx60vs6XPsQZg0al7wkpjjB87UbAYSKMzXT/wMfsXfFziEhSt5LyPWBcWhkhnewXSm+iSriMFney2ehIuaBbVg+A+b0+sdJFGLFF9mkYXZD3WnBpuSmhUY2WOYGuM02BMxCjGx4dewwEmgJzF2jmXia02/mcZZEWE44NBZYeBV2iLK8IKTp5SoJC5deC1E5CKEqmS7aD2k+NBk6F09XxZcjl6BV+6IkekDcZI8NKaPa1gL601BJOp2HOajhGVNiRMoKcjsmcRz/Myi69vo6wQCu6FCWHRWmR+QR8oeSQD8wwOqQOujgMIHlZZSH+rLMXANEEY1TS3H9zMZj/H4R5s6Ol9EALnLVe9zbbWHOkPMlIcdaMouVgOHOVaKg+XJ46fT9BSKb68UIW07UYSCJKQpZC5PE0NJ4Hyd9cxJx9gsibibqxxmv59zIYKnKVCj1amasl2hbxy8fYFHFHe1xxggbUtZ1wMcKF4fwrtnd52fpu6YTgecjBxzFxT4N/yzF6JgdcUv4wZ0GfMg1vxUJ/R8jOHUHol8Ev2mb7hZN8hCcV9Kf82XNlAduAD+Ud2fiT3aQmSQqOctOPUaBCSKalelTMPA1TCjpgxC+M9E5HGsX7kYVnLKM4RTCc5yAnha1R7V0zwi3hYy5Uz9qu1jBtKpZVqM0TClX1fIEnw8y4sKFVwkpmVvbP8JEsOFreV7OFUpFeLVxb8ceYOdd3oOGAgNSbulPPhUMUp9s1tTzldtB5qhN00ZTAKJsWHLuv5JKJrj2l5WfQsB2pgOLgdFA1npDYLwZscxD/frKAcG9I0anKO+Ghzek6uPJ+5PMx85gZT8NxHnM7F/We9QnrD1RG+jfs9ZQO80VgICtvlRTO509anvsMLPawYBWv8tFCjVgH4eQQV/WaHjFa/mxXtOmh3ONMn6SdQmwGcLM08o4sYvAkSKM9iiFeNtFFsrb1sjXQ1NdlRgMoEbsqCzMR9pszULpdFMQDx+YLEssxWxdN7v2sX7XbST22jOxz15GlnWzup1kVgXzi4af1N+xGYL8NxL1XuhE6BSVNOVdL7u3IvtnH4lGdtPmnJUAJMOblmxXb77w+R+OeMntMFy502QEP90C+4KybhRtcDUQmg//f+CFjfcaQ2khhz5DVRbI3DSCCKeZkOLiLuCB8nu2mMTzUuf5/tAAuM+tfjtW4xaeu78+kH4pYmIQ17jAtRG4mA84C3+ssaLANo/wRrocequq3Zc+iEF35fKQpFOt/W8VxN270NutBXvaHIxC9+c3UIxR8WYZ1/09oIXoCHbcaUjUoHHDvYkiJrzchxiNZI2QJ2s/ZZSYCtFWcFRVCCUS7XblskrbgijdTGIr6+DaHQUA1La/hW1npi9DVhRYD1Y+Bf33n+8HimI29CCWnkaLCWxpFZbwfpHLPI4bZXWHFSnUCinEEZqA7XARKs1eqjiLK2VebFsPpEXhtZFH8PDzO1SpVkFgwJHilip0dqVKvVGsfxxvBvp/3gYJ1huRmOloHmXFU3BmMT2uTNiV8kz11RBbkwv1pHlAjZGdEpuUfR7aW+kKwwWippAoclbY2nO2vo2f0qBcUxH+NEvnWoDqrMJoEAQ498ExxIPGlnYoPnkitZdTnQkjKPg2YO6XRmMqnpoMhzhQN4y4s0Il5ylgLB+2mpFFm6fhw5qa6XHU5lOcqNa39ABHO3VjFg7ebeRdroc+6kgEowS1Ijy1fujXQ7iMSXGpRgX88WPSVQSmzwCQe2qVF6z/kAAKAAMAAAFfAwb/k8HFIOUw4aECAnK85yrZNd3HcxYHUschy5nc4Zd6M1wLA9WAgPS4PiTeOqOpoKclLByspWr+3cP5uA5gI6oDky93QSPkYFRHVJ85dpRhHiSkyv8cvQoRBd2O3wl3Kf8nFtBZs8KhlWnVV3lpeLRZg/Km911qVqFHQYCA5UmHZvREydYwJb9tQcdIRIbn7LgK2SsqkArw1Ysuzerd3Pugbxtpm2TVgMMsgIXktZufpgBC4sdL7rrfoBWg1SJ5xMDyPK/TuA+beIDx3Hi/l1S1asnB3ZXUHTX2lj97hLWk8BG8wM48GLUuysA4/HZI4YVjygMhfIVLOUWNZ6gGIsNozo4vyEl3DSltMFeV/k+090slSNKrqSNBMm+ReaiQDLDdzNMmBvg46IzQUZIAUDgg6Z4xGVXMhFoPN0BZHJ0FwDpTGOGxDJHJkk13htl3F2nOs8H/kAAKAAQAAAAgAwb/k4CAgICAgICAgICAgICAgICAgP+QAAoABQAAAo8DBv+TwceArSvSHazGdVDUxAlRqlqmgID7p1A6+CBDAKk9TOa5gR0H5PoVQxC8ArXZwWlV3+vGg3WWV7ydYjgG0FSF+1vWn1c7AsdhypNqF4Hk70XDcB7B5jcW3T4y+fclMWS1mplt+iZNGf1RYJpDLCASs/qytSN6Hu7PIdlDdEXdmGdN4cy0pezrChod88am033YiYOJpX/5wiTOxRV7NztxCrc/0EUTtVLNZwRvNFZ04XhfE6INtdOFv0OAxAFHLnyOefWRKxQVT+t4r43yTYA/Zv02IBWUgIDFWtAZ/mcRO31cC8J90IpIPm6BKcwnYddPhCWJ84E8BFgRs+DROs1J8HTMmToqOzqG4YCAwOAW1q2VEeWEFGLx5Jdp8pnnvIvJGX1JvaFSeXLgTZzCmrHeytKAVNjO09tz6KhnzwZoo6APiZAqF4NqU+NVz5Uqa6pzZR4MlQKLgjU0xcB2nNx07aKIg5bugnHk7r2+EpdofTmQaXuSEw/ewPs7APCLLM7ipSJ8d4DIcgAcnNbW4soEopfIftj5OBSWc4zEJRerMXT4T7TvnUMzsVfneXboCApfd7ES4AioYYF71mQjDvrJ3/1DxI6bqIETT+0M398WZWXqDhRNkFwpRGgWllQ69SruDsZeknKfsJIwr/t+V04GMZKYmWLXgozCLkaEN74tCTPrN290pfNWfJrUz0wPvcArQMtqxg0YmP438X6TLFrihFi25eceOMgqnD57j61V23d9r3PmLcgMklUKkP0ZXbnepCXuBAd/xksi9b1i77CaXyoronXFkA5Sk9FHa72K44JiA0ifisChw+R9ILZeYYZjnjT1oxU7MbZfCdJTNaJggYD/kAAKAAYAAAcEAwb/k8HNND4BwEOPY6ArjsJjwNtUl8UhJ7sRpYDC7mq4cF0wWWZEqnJIkP1milUd4tivRaRd5ICA9oLuzQO68LMgoZsVA3NaU0dvwnQ6H8cjkVclozJq35Ca6Tq+yyKCcv8D+r9AN3kb4wuICFDzaQVS/krlmLmZQ1wxZ6eSlldw2wKTLXFD6uausWRIPFPU9nDJ+l/xlzmT3BuQxBSS++9aXMPAyA8M3uEaCt7PwupL3fg7y8JWaR2Y98UFyp48+nUg4CiqOCr9D92pzLhTIMTSXyKr8XLOntmJzI8pEcitWv8HJU/9aDKu0H9qCbgK0Bze6E+6ctSW7AsBFLRRmfNUaWS1mUeu8MGpOEK6ACFw+e8WEX2Detm+OzFDoh57RJwnDeWSgfEHag3Z/IfsfNmi8flS4EiQ4zRKokqYvEiwrZcUPPIunvAreD3ycW+GlxQ3SD6vLuMfuGpPfrgtLuQ/Xz7t4krmtbW0RSRRiKcwIlS52BSc5UqgUwtS6ptWe2XVuem7wFeAi2/QhSqoj4yL5kvtrt1JgOfdVeW8cyrgDT1jZkrAUqjKxfZ9BkxsIGVkiKGMyRJUxGEw+qxLRAASwaPgry19Mrwdk4i9AOCJQ/hjsTuxhvmUYwL5BNSrsBQ9JQXnQT5LUgKfgSgpM2sq4baoEAc1Pnn4zxO4dCuYbRnFyFeaJwrMdcGbSmd3uz1NLNMSw5XBPqYOJa/Qwh7i/e5S/gLxuIz0yvN8wNPTXN6xZi6QwL++QHzn5u8zCwfW4ojK5GWJSrLoD7JXJYUm2UGhAsZIX7yRt0B6wqHimkzUQBuDqxHz8cPSrw0rc8JjVW/gNkiEmEUVh5RP283bYqfXnDWS5igtp7aXv8kMyKWQIlZ+K6rDrbHJtZsOt8nmN7sy+ut2XbWMSeuP8tRqRuBzyHqT6Q4cLgK0FNQzzhd+pvKEmbAVxsKd/rZeP01s6SBTIPRZqPu4jB4n8v4DlAAHBFbhBUNQ/0JWsNRkdbW7S2JVrVbgjwrVvOybFV22C3dNZS4JaOwbwg0/pGVadoXDwq/+Da+lyunEBg+IhVThKaMrk4AWHjB8/PZ4jPsLgMQiwFTJUc0KwPTWiz3KnyQaT64Mhd7VKdb1gbIFCKJjoHP1IlbwBYXyq1ALWCjCZvMTyeu4HuDlvnhM6swMkhvzc9Y3W4emYa1B9qT3AZPYOn+WsXqZIBMV0KGvB8AupgFqgDb4wP0IvXmFuA0pQrtdb16nNgIl+dLZLQjK2L7+iHFCX5oO8F/MToTEWLItzj0xmPAG7A7Nknu9/QYjo17y6lauVbAVPht2g5GHeO6ezY0ft7e8hWq60Sr9znP4xt0ZwL9vo1geEUm1pY2jmWRbG8yDXPgTQoW8ErAbGOVNRYB+geoHnJtIHfc9ZuUWrIdLAccVZJfUoHaotLyQDwb+WIPHX/mZRaXx1AiSS/nnHznDysBMy9uG7WMqaUyLE/iGiz6/mvHEOzXd8sJSP3JDxYtXnzdQHFkoSTju4ztiuuDTW5OyoSLj8Gc2g58tifzmwRro6fz9yySEvBzSq316MgkOhCK7oVcQZJCwlazcTNZpuxq+YymMVIEYaqWi8dXJKwHWnIFLphqVl8h7f7l/cv7Tn/i7elEeEg/Q4MNUpQTLAyRgOeLM4H7xKGagL4frbY6ei+4H8IrdsuStmS97x7CE+Mj/FNR+Lz4bLIFuQgSmJeMzZEmYUIaHrIM1RBTP7al/6/YqK6RKFU2um18n1ZNg7ivaQSsQ2mG2/J8VEKdLMs2yxrSSM1Halk7pkAywiZyvznwuZ6aXjKjBEVYagOdc+SFoEc+4HWT1VrcGSPABPTbHjqK0UQetnve2240Tl/22AQEtZAyL/EMHCwgYLzBckYLgKHrTtw6oJl/gJqgLA7O0OOyd1/y1/efgt7Yt88MR2Bk43VsYy/YQlqsFWIYUKGVrtYjrRmedgJfVdA3sWKRY+rbgpCzy86nopUIKD0wcc/x3WwnWvFkc687aYNmFgG9nb8a7kLYVH8Ou48fr2lJWDmFIYu19niqehNj0we5ueGU5/pgAIEz9Wo2YQDxo+MeY0fjHhMHY6pa+mJ2LcVkqgsVJusZOc0P19TnQUZG+6zbrkEux6MCjgkTPWeVNiyqc/HaBhawwpbXSyBThBfJUFaZNwlx0T/AFeIx30ONkYeDcKAz8FJ5gGCXNAzEdJrE496FSF5INeX1bsl9+4gn+yuHoa1XeWkA2fDnWiSHkfGD/fV1xrvuYxLq043/lY0JRo06bXGmE9q0cbw51z34UgIxqXaqOw7Ij74AfPihX3SXS/iL/YZZL4qR7Md5hPC/ylaSPreDKTeI6cvbvRYoTvUbTI7RWKsjvFKs4ER15SP+QAAoABwAAB4oDBv+Tw+B6DWyKWDcAR9BNCuOO3iobZnrGRnK2gXcjQxCP1uWCOM6GIw5/ZX5HM+YA2IG8a8E3+a4aKHjD82krCYaAgPumPg3jB8WowI3CEk46VqfKSy7tS/NwxE7kJGMo1Be3160uGH1Cro8SHURETINOV7qSqzzK6UaDH12AbEGa936Uf1mxYcCPe47gHnjpfdOlwRySAO2OJNBNx15neICPzb37vMWRBFb9kFJ6nyjL9+SO44wTVxomA1OadSQKJ7ZMzTCT4SQ0htJcKl34E5uFT+E1T3h5Z1vPRIsOCQc69zBuuOmezH/6/b7Pon+QKzR2Po2L5/zYUwaP41Ewdp1WOhy7TjlSqhOLblJZ40lqmO3ahETzSL8mrVqqaI2HU9+41JyvDriwqQWkPaak5Q2sF4PoS7X4QAHCbFSqSEZ1ViciivI9dDameZpjlbj7UQCE/Hl63hFQRo6myl8Awn/cvGIbMzSoQs3JMCFK3biOar+/pKZCFdkQoacHSLnlGRANVvtHsejT5ib7mXW0Nhra5BAgGHrJQU624cQTuQyAIoEGSM8hyMeT9Q9CeAxKQWH2YPOOVWu2muiifxBKu6ADsCqYYYlETwiHLC/pnlQSPYgmN9QjNe4O6MExhMJKR9SORup11halP+yf0Osgs67Y0tb+Y0Z9YsNc2TLmrjvHNmb/VZtMuHDQqR3J31tItqbJSH95IX98kT8zzLa18UR3u9OFSefSJd1E2MF/47YOLJS9HrudJ1VRFldeftmeroetqWIRs+OrttZeINKY2xKZUA/Eif2fHX+3RZYYrlSEk54U0mqnNt/1tIamyfwOTlppdem+RIfAV8BbwIF2elxpKuoTizi05L4FxRak7cgTLQqu9cAbaAcGRU6cWQEQ/0hG7cFyNq9sLIDF5HIA/q4bl18AFzXd+ysklgSdsVpSOSuftUS7dvDWle5nj1ox7tg0/hKIFotEQ8pyhDQTfMKyxsYU9v2ziECL1qn12q70fQTchpaUi3by25XQOJLi29GDVjIqW7glfQbAos78Ht5AXZQy+79ykQ1kxNKPe9+70DaYRdY+TTONB73qVmv0duD6xuFMZ8voZ+0VG1ak5WDwReeLL9eccSmJ9h9J6j6sgNahp+Deb96Da8QEF/LZAj3pMagZqzyzQlY9vn36RS/uebEkmFMpX9GcN+Fu9fqeq6U23dpwf32DoKI9X3oPgMYh0aAQt9MtFdKoO/hKe184F9ksPx7T7qA9JvhMCofHEoLZQ4MLIZ2X0vCK67p3B1PxYBuflOOQC6gRrEaXqvvtCvZ2KQGpyCYmQJ/XB3eO1SS/BHJ02PYnx8EFzfdZXPBowHWoDtwAijJtmovg4SL9BufTYfWMXQ1m9M3yuTtZ+t+uY2YdbwInSXgN1KzK2R9L7qxoJ+1yZXyZ3yvsXHrYvSC/Ez6rcJv3NhqTu/TgONVfXv51+IkeNdQqp2gAz0Kxz57iSFqyMhmQVTtJLwS7pLdqqEnioouuRArcBN6oX7+qrQ6NclvBjxiremwkICCku95Wd+idgtXdshdRCCNFaZlD2SsCzGism4w+3xGZnA9cqn1nerbf2fujffh+vYy7K7tkvy94WTwW1g6rYInEX4QGhAGMV+mElIWCt/bKTtpgOg+D+8/Yn020djOMORRR+JN0XtWeKn3Mm7WTd9agPgc1QBHUK4po0SuXKjJzNPvsznp4hAiKjWqtGctZzcCCEAiqDA33J+MF2TkKm1bKlDTC6j57xYDKpzst3cjUSW5GxlWwMR10NoSMJGHbIHO4Pz45J1/glrLXTEEfGPuWtrcnCVZnPwgIq0zukXGARxxv/gFLc/wNIOAd5Zu8ynlW1iHVGqk8XozYJ77hmVe0ZvShTnqFX0k07TPUBL+AHjY0Y3ZGmMtUHvBKTbfEXHBLBdfejMoL112fafYBabRZgeZOxH9aeoeAetaJzcJq7ONUIPaMucnXPsJdh4njjYMrZBzV7p46SMB0oYEypy0UA2nUW50Yc7fDvddClmtXNyl5fsM/pDhTq9dy5vb9uT8YV/bR0rNBtGOYwv7aMutOsVDuD6x9kA5dQG4SnqZQprjU4rrWgkLnQoik0RNdYOHlzyoN9/jFsixKYuTN5FG0dTpB26HKHQuU25WGqRXh88uSDbwLtjVE8yWigBHlzIGYCQqgZAQ8xGVrz6b/YgKFtrN4VeRy0YM088coUqsURK8kGCBz56BZVTJJJ2W5FHSMulNnSxmq0Q8A0JHdazue0u+4ZSXUOpFp1JTSO5wZCqTvURGUuAUvDt7D/4GMBsa9/se7YWtrMpKmvd26tLbbe5Kgoxknd21CmVi6JXplC3MadmqXrjVVDmWSJhKBDYfZBpAJsSh9UlR5/sxO7RbWgFMJosvsDhOSHESsmnDZ2YtEkQy63L100Gdecqx5BEX5LAjzbR1tjYZxOxh+ZJVIuRNBlhCJQeNQYF5Ze1gm8HvY7TnNWnxvWbAkQ0LqvmoFMff4VdmZYR2MsmLrPhDqJTKwLJ3vSYorHhxcmbEuU6XyHvqRW3ilif0pIrb/kAAKAAgAAAWSAwb/k8Ph3iDi4OWgkiYNIb2uO6B/bXDH+BXbh7Hj0iYpITM14QHlPZxgTe6xWx3xobSFv/WRdQlUTfUQr61B0lMhPSKRBxrtQNKA1ZUy8eT9JbSrRt6LQ6osTTbmJ0cjTE9HVO8aySgOWirZT3IJLMWaC099w4G6+sdae5pvOGHnDyd0jHnB/KnRL98CEfCMgFFgHVEPxszx2Apc3C3HduajqLpCYCUHS4CA9pftD14AgQUyMPWdLncLM4ME5VztJO6qUjOYjx/PlsNzPT6P8ig/nGqGtsy0N2P8p/rpocZdPnet2nZ0Tm4+2qkB2izjJxapotHdqWEWDyByHAz/byyukM5Grehjrak+XPlPnMziBsqX1bbs1fhHRWZgTH9uPRdb1QUzLTkY70xRGR8VW/9yQwqrVO+i6ZjKHmaY7+zjsUxwT5DYzWSHpa21ZKXSXPxKi3RVWQOtbQ7bPwt7kqvXQjQDcEJy3EYNl4LCCN3xKiqLUJn4d+3ZEqrkUJYv5psbWevzb+ORn88hWidrYnFGn2s0pi/Z07CCMUCh7Xzgnw+UDsBagAccqcCk3AkcSbzko18NLR3B6w01gOPtQc0QDtfL++5dd3bg7VV+TqPxekKtP7E5yb/80YdS/twxtMXSbCgbc2USYWUp6HRYEAP1tdI5avJ7a01AwMXCcJfG8CTVYVE+D75sD3z6q7jeHriMuLzZ6QOHH5tHrpFwO3fa2Xk7jJ33EGLlc+3xHniuAMjIqs9U8U4W0mwx/HljJd49OrksVGPDVVjo8Pk3Ekc2Ggsj1N7q8kTmnKLSJlZrXmQv8sYAAbcCt6zSbn8p0fnuqICcK/YRzMtMlTUYOrV0vhZO33JtGWdw84s0LKs2WpA2nZqud6DF+egh58VXkkSIvnjgFDexySGJlKJsUm+DusR9iyM6uykC+SsXWgYjAS3j3r34UnAwwC60D0NQdwz3PmPClNJ0guoeSygJhzruXlHHNlJMz8RDkzJ08ceHWNlVT4nh7qLYpQCq/4Sdl0O71kzBJAVvVBRdxAO1VZAdPOGNkL5MHNNr4ZCHrYn4BoDMk6Bjk2KqZWPRkkOh90jBt342z9+JBPOFMAEpirGqhM4Lt6mVuevQ/w/i0Y6s3JnFU1g6QTr9Q0VI++cHBNUGfBnbBPGk3H+erqaku7mxWcEWxAd1CtvRus7uJJph1rN46/9XRvMkksDscbnCmAPqCgFZbQCXzljYtmICAHSnDmqCBOOR/A493eOvGrI9+mTDt2QeXGS5J6gREXvc8U/QghgkWyNHjK9zkv7CAU2QMCfXe5dkj7+GlxhiWXSlUjj5A9euIOmt+Vzt+EykiS/5+8wWosoxEVM8TvVvPJKLDQCrsFQN7rHbW9eZLQzxgTK54zhBcmqKi/yCCJ90L8kwi3NeicpSFerSWO7MR5LTbyHA/1+eW7dLPFVshqmA5B1YjQDlALOPk7kKl7Drtfnx7YejqDwlmap0tO6VaC0mEtIpbTxzK5JwOtLfeSqAvSjVRaSEgJU1ssfVBbQhysaUkv9GpIRflblgTXgyosmfJCN4s+7oO31KBvKcccHsnAZTH1G6TtiJZQP5Ppj4oIxDqV6AVC2boGTbzCSX1pe2/i+gJ8BynbhssO3ltm1udEROis1kTworia0okggJwSG3Z1jI5tCKV+mz5U3BPJXMe4cIvcvVbcCsmHMs26tHigiFOGF8tPANvC3Tc4Ze5IZ3FWYbNNLzUELJ7RpwnzLCY9or2QTlBAONuCBBFuZqIS8x5LC9xTi4wT6uMBKqgniTWfOPUbp5fFdhnJKSTQshvK6HYlR+UYDtHyamtA/6SBu1RCFMMe7kARHxSSojX8qxIEY8v3GM7T7GwN+JLAMVimPTN5+f/5AACgAJAAAAIAMG/5OAgICAgICAgICAgICAgICAgID/kAAKAAoAAANeAwb/k8PguACjA0AwUfcoYq0gOQGo15VBEch2dq81T9dfmnJkdx0r1qvg1fvSJyoHB6XBoGQElvUNa16F3mkKloCA+0dAVcHxKjNJaoM69jJvLb1yS4aMzgrwYPxcvl0YHVRIT0IuerZ1vAdB+UXCl2jWqMLuj0frdL1C+g2EmRoQPVmSGKlcBsliBfGrUbL9vTA9MLdwzuTi0Stp7y/2nyDemah9kJJ6TGM6k7ZNaGzGEH+lnoTw/jHHb7/gHzs5Qlnts5YHC9HYZjZ/fb+o7nFWMtG19Ircvfl4HhR2fBKCGuFIm1VffC+tvBmE5VVJxO9UFE6C0JmkqwT1HUU3uobhh+aYpxZ3TiIZrcBa4IuYOjYpAooaviNIOsAroMRfemOawPBRgMZiZoDfC8psBet/lQD+KKT9XgNcGrdv5HiWDnzdPTVOkgTft8swEM7Ty781DdTysACVfcIxF56YbdXHud02BRU9NrRrILTuz9dZPkH2FxL1k1CYYYbOUSEsyDIAVYfmmXbzP0XvR+SZ5TtvRajk/eZh9+8tmejDjWxSPJ6eCV4ZPdjScFnzIN2kgOLMUHhx85HG441NP8GFHzpwoV+AhlRo80CVRWYoKkoX/aUx1w05hk3I9ekTXrRO72xjywzb2k7ljZAcMOLYylPZpcB2hMPJM/fezEZk1gDVxUy/+qIbfJyjpMBf0yY/kyCaYIGd65tKmRQ9b0weP/YcbiACzFJoFaVYo1sgoFLAQuvs7A6BGV41+FXNPuVnHDVyPl5zkCaoz/neuYVPrXctdYG0WGG0kkJQ9SyP8gzzOcCd2+z8NRGFPw/kSMmTfDLikNPjrtG3edp3w4A9CMsxJOOqogpMlMUFBt0iE5W2F7GtL4ZovaHGlCJHAGsMyXzsREmA4vzInGhuHfDRGtGOgwvIW+iQJDzJeRkdkqB/ibEzBuSkewzLpyGCXG5POTm96utvYcs5xlBGyVjd8tpYMyVWaUMAQm+dn0tgGB/hgPNfAITpCvWdBQk1EA+WE3SwskgklX2JN94Bs4aNUJupT/JyZdJUwhKmfazMkHzpnPXLTsYBXzNVatEYyUvhBD8MkKsrYYZhvMkqiueO4vpUoQbr3Tf1A5d/OqAEoYYPGhFA/5AACgALAAAHlQMG/5PB3SYfBQQVIJc2PlOjaXO2ReM6yWcQ9OLBTTxQ1EGGvErQd+PZp/jm3d6/ilaVSUu7IpNWALzr2cWh5nrCmCesLLMzX2t+kt9TvVxu8EzezGzjPC+ApT26POFEzmYWODpn5IzVMuCfPxJmC16nu79HXJdy8b09Mrmr4oCA9qTtr+C6ALHS4Sz/Oxncdsa6YLF49teAKZ9KU6NTAPhRmc5xJJwSciPxXByUnBeODQIG+zCgaOZ/QyIfRJaRHxvFU4N6jxcx0qBqiKkG+BdszbJ2+Y7Vec5+E3Cu9Me1NKggxdwwRIhq9Ic06QDjRlVtskux7e3mF4iQ0KJCERTRwi8VC4T5Qqz4GqyneaT4rPP27xJHQQDFDAFADdrjCmGU5aabs7j0nfPmSh4FFIkkpYfgCGpxOmMFi6WHRgIvPDzK6KYi5SghfDyYAIvZkfH81mNk0PaFMY6GPn8iJagqPc4JZZ2S5TM9vKL/XjzMpSkr7GHh2Eb3/g1Kj36NaApH7Jk2W1TXbMI0uZdK5w0MQFn7M9ZvyNp4nL9WKhD2EhdAZtxbkONYjh6AVgkJy8/rKqjv9SjFlwGvwVKVi9NNBmleapX0g2YVqzktOFHhHuxhSmn7/tVd373UWSCQYtXqyBtK7Ec1+Osg4puKPD6qslxUp3wd3RNXiAUOoFQjnTaMaDFHg2+ZY8BbACdgXO4m6txIxgaTTUpWV6sPWU/4+PUPq4DItX9WgEo3Upef+BqNlT1mQxpbyrWSTvZBWRA3hgLcJYBMHJpZ8pl+ZdppZQOnLXH3kBvua8lDzLYo8fNR7fVmCZ1wgLoT4QuyDLYxMmjnsPPyQgQN30XJZsE9n6IYyQCzpmixfzJDK7hZxdEgwMEnj6juqEPMdmOgkkN9qzc7Lr+Oer7m8/Fp5+yF2gYm0+NjWhVG8nk8br1/xHHv+yZwxiPRipD0cQcC2ANh6zw5zCloUIMMAGOR8NbO5X7sK6pY14gtNMy4taahet0b34JM0vKRUZk5jZV6biewd66U0PKJAd0GA7Xg9Lk9OrpyuQMoeJ6+dgblsOUWt1kbWMmZQckEbSlgRVP2k7i3drYf1xBYGtF7FjHvt2ct8VldjulHgO3XZkE//20lbY/c91ZFHF/p/sJcIzwpAgc5CiRmF82YCSFptSWjWP3cYUfPFPevUmkucq3NlebkQLCQNQEPDJU0DkKi1/3O0a/LhVwOgxmzOcCVi28HxNYoLchHdZ7iRPzzuuae5W+BD9JoB8cX7SEpISd/Ei8p8MmfwHFAzhTHWHY9pDuIXcJkMZDrC7ystCrKHZZOU/eVccLL3iq2Xks0O5mwNnR2o1+8Dy1bvEJX8dIO1gZoox6gGgIK3W6tF7VyO4pEySOadMeeT+B2gN0MBw8AtAAdIU03aRIUCLi24/j49UZiyWJAvc9JtEN9ePOnmMSd5M7jPargo4JeR3a5VckyRZ19VHO8WQc1Q9R9TOoMeaKLHpOe5+Rx6qoyVaxw/RbprulsslVAhfot5CkR8pYtQ3ex15zcPT6x6cDzMe6tdhL3gvOACFqHWxEEZOZBGlYyiGwSIugLrxrib3AAAwdjyXxh8dUZVGVAx8JHds0x5Gd6Yjvzb8cHlAYhzkZRSoepqnaUBIeFjUC2wExFF2Vxa8kgZXQWXXrYD2HCJgAlKk2f3Ix6qhWpCPRmA07+gJUcTYFmYfPw7HDiPsdCue8ZiQIiUeKdAQYS62AL5Ud8loIAau9WvJAo0YyMlyCpaNzdLBvuDVaKjWgNhm0hI/Kq4w7zDr9SUZBm+PvsH/H+HTPwzX5zDbCavkMRiAkp3NkS9iZ16L6zkxyyp1aD68gBpMBRoqOKeBt8u1Pktdos96PnU0l4W3HMZ3UpnuKBvfN6KqqL3DCmBQJySY5vVDRUhqjy5T8oddUjRPZPvUOdOYv4IA5z6OfZ+0YpNV4GSV36a+c9UdeAm9FqnsNJ5B1E+KwgPcAnrrqmuavccu6xiEm2kPVzZN3i0IV43LzYF7WxxqEzX6KgPNq5vHJ8RkLii1SFzzpFNkHFF61+Rl7Yenl/6li/84yzQc78Lu8yjBoMnvNmAjvikioC+K14sFrrZRSfym5BzQmYIW9m44HrGQQNY/i6tP1qSVdv5EYlJZWVttTG2IywWrvyNf9GQ556ApWry1r455Pv+mPm4/jQ1qcbvGsxnpq78eIoQZ8mK4Z+F7JouRqXGGJkDOlZ8rRhmwl9PwZZQ3oOoAOrVF3jEB746mLFT141qrizgZrj79M/6UYyV/h/wPImrTLjPOQ90V+UXli5YON+bLGwgvlYf13fzgaZLkHSXIg1JYWVmGiCgExMfttDIC0TrxWuFBeq/zFuc+CkZD68QyQ1BlqPfKOdwgRyHEdEcvhNE0blCJ4t0+hteVvNcL2Leh/YTgVMUPsJDtn5tE8UmTvtw0QLAM2giq/0FJs+hNg2o/p+lUM1adMPK/liNqzTt27WC5aVx2BaWrM2lO3buwofG0CTCrUUjDLSAMm4GkeFQUM5OInuGqjWi5FwR9varCMoZov8bIz2R0240D15GfnPZYCA/5AACgAMAAAGWgMG/5PBytByyIgMjgKh2Ljpn6X+S2j82FUVRWcjZYrBUSSkdj6hz0TVeRutqL5WqaxMlRe1c6lJgID1n7oNB8Vxi6uZODziQRQmkK2+BOLEMZfloLwf032NWv4ubzZJP4VgY8ICcSAlPXeBgqUzqUM4gNquvIOFm86NcIRBOkJzWgC2nGCPyQOOBuq69hNQ7fBKYWoyFIKmF1NcAvQTVE+UompQQ/QsAZNePtwXhIj3rm3bfPhUrsa+X9jiQCyzcSDZryv9UGSph894KADs310pXKga0/1KvJY+Wj0cKzXJy7uw/y2ND9iBH5eR/0rTpAyXP6DCSP9aJi2TE1vw7ukyaZx0RUXNR7O2emZhRKtOGYTs8s0apx6M7YmiOE5NIDIim8YPUbcK2U3Nmzv1pCPDoIJmmZaUlE7Il4XCdRFKDVdUIlVCeuDkEap84k8AQDvXkp8aSjAhjLJSNYT602+kGn3ofIdFhS8YlKm8oWkpRK4QbAjrrVdjyIuFzM7zl8qHrzaweJLkWmb4il/ZQUcMbLV6IsxZSEchevvZUY+lgasjNPjzBKE/Ha7QLSDU+TTBXiFKoXQsitYnDcWqZz3LJ+/bX9AyXBBm3PmAAASsxHZ2wwOyltxBI0tNoBcDjRyOWkoSxlY4QlYioC3QjCFB9sCl95d0xK+88G9BecNO6pdJNEqNFAv/ZgeA6xwx1eBjeXx/GDNBVsxIRDHCL7g3NOH9GhDNz3hzuyrSmqzwYwNDLcQydbglMiNuEVR0C4sm7HxISeSluFnovO+IvwIYy+lPp0uPpKESwPdKcR1RJoXS0+BMLQyfz49Bx8zEaKkjFxNvxgvmYD22abQqh+W6Nq5Hie/sdLEVgSyhGICZwHMygOtM3Lx1Xv8gAPowzRUJwHKHLBAceIsmw7U7ohMhwwtKykus7gxIKpjNRGswjdwSR7Xq7pHM426VL8WZE1rlszArcmOYgf8PAs2kde+SLJlFxp3mLQWK/KsHUeCvj17WgMb8F6fb9FKoVoFRS3zIHbH+ybKkUloKheOB2VzfoH3vJ+p7XBSLFr5iWZx2dVX588yBJueAzQDPr7Ath8yPd+g0jwhSVRDMxIk20NVHAeYkYtUWwCW59m1rFS8LZ2mvmXyTXHLA9D/7ukuCguFcnwUyXLHqRDo7DPA168ptILIwwCsgF1KK/l8LjdC8CTsKwyFzp0YOyMuEejI/aWsTxHAM0sRFj6rQkZJO6mrTmRApeqtM1LsBsn1fkessk14AUm1TgT9JLZm9DZAAmzI2vhOr+0g/EU9qP4NIOn8xVNHsau13GpBrmEMhuTk0lWJT7Nuc5gvbIynKnHwhSdUQ5Lbfn2McLqUYb1PIfEavPof7anRmerM06ulRd6SVEz+wPGTHkdfGBp09kGwqG67qRzfAz5lCcGzOU/xb0ogQpHQfwl3neLiw7Uy5S0Ms05LCqy6T6xlE+vUPVQSXG52UfuklgAuqX5FFF2PTKc5TvNKHOnlAr0M1u+IM1lHiC1aiEOFh7UPzslgtHh0oROAVH2RJzdX2gA58EDM7oCIzHQR0v3y4b7kX4NVd5PqFyoBOVJeUgibY495zewcQ95YOUoxpin6CHig0CBkAzjQim73dz9/p0VOHxJgYzZLAlQb/S7kdqS14BhCVBppgj4Jn7I5mAoa2Vb70SHaGfozDnJZNoP8ccNPeXy5VasJv/jrxdUaeR7qeQt/aREo8T6QQHUwAVxH/LLaseVc1rWx4DYHColphKdfqhS/7frU3KgGGP0yFfOO2e2RlxkvBqQtd4BMEZXqRNeFJamTxeNd3vx+fKXEJ61fgg5JJU6wRrhZseDLbCBusfrzLJCO2SPwqJHsidxLikA5eh22Abos53j9wfDDqbDWpwIsF0Q3iwsQzecBmRBDNpMjwFgB5eksrJ/8QDVuc7pQeCZWhqgMHhtu4zKSvOrXd5xuY5MnebyxDGAOP3bZkQihzGQ31BjG0wuyAfF1ERx/O3zwPkuSaO5cq42p0DiK8/NCjCQLLxMiaAp5yk7P5XOFbfvEZpKm478fDgoCyODalYtYcohrPrOWgpG/KeGKpMqFiQfTM7R/G+bE2h3auEkFlPrgdy/CabzI1oA0ewLCoRG2JhCvGce7fjMH7pGEZpBu4dx4E/5AACgANAAAE9AMG/5PD4NAAQOyMvRuJSZ2jWOSOf1zN76VnHt4SuXVgS6zEm99F6UmK/wu+17XV2Gxmnqub6+Y4DEftgMLOLeLtHlQaVwBpi4CA+tKD4aCD4qoARZpiCy+6p8+HPmRAJqFfm/hy5mU0GwNklDpbdv5txE/0fzmTE/pOOXSXPTFgaIAj8ENQX/A2f/PB0fok5xZf8MvfHsH9v0hexdYZqUSXFPpAJz6f3uNOiDol54TnTJ5YEaowD/qpGy0Hl812zxai8Rpg1tvTycpGJOEjidhxRcUWJmLNR4i37OyTUbl0zMmNy+Ws9HUdjffG11s9U4FC0iqOYXHUcfXWIidFoDpNR4o74UUK2ZYqMMYNSb36V5iwhOBr1Q0kSfD5mKMa0LQJG4094hFVI0+ybOVkd0R+A0Tzzdyx7KQZmBb9nlrOQ2Ar+wzgH88tAxGAeBKepjSy4Ces8isFBn4NL3PY0N906pRDc4lZ7Jyp+zBEF73hQUlfC2+iOiGdMeqzIlcC5l4iqmtIFT4Ok+CovojOLK1NQ41uS/42wxGAgON+V2dAuOu2KT3ABIHBjdpzXecLkRSrP27t8OYwotQEmZeMLr/Sqop6pK4yvAd+mJHxaABGa18Ba7iyU7AEfnuePuUrrJ9eOIiJqC04I9OIIWGlHyYD9OTupS/osOukc5WPfIbDU6/1TYKGPavIJ20IMbVM5oLtLGxMMc9eyB06sdOMjK3cnsB3dwBE2MRhjmEALC2S926KtG0dqVKPuKDtvrMC8HAUSmpy32JwRNcMa9FyTh3x1qtMHvnr3LlOSdOd/Mq8CpxvK/EqAPdz8BElZRK3v14jJbE56KGmqPVbGoZMiTEpvgMU0NZ44Kbhwrj6h1bEWUXsrjmEM5OT4BjauICAkAtAEsmqdq4NC/O3MZCpl/8wCMB2kETah6m7li9a35AE9KSQrgnxw8v2D7iFINTm/I3dEBEtRpLkLs/WzNDRBNfAKFtlddh5DPuK1tQtVZukMKO07hsTAv3h+8pt5mUmV4KoW+PhgL/70QpPssUwEyndaCfc8JNuZNqC70U6WU6o33VS9vm/npdFPzuK7dIKUo8yNOR0VBsX9t05ebqvTDy6lh5ymy0Ke9viFWFUJfNJsSC+LPGHbD9O9MVxVtORWwfUL7WSVoB5+z27ePKMnkjWajAicKOCeMKwp+mBfMIXv57V0iesukaMcbrE1IGemwG/hvenD0t36ibTu/d6zJpW0UAxm700MvmTRdqAqdxCUO7czZim6odNTntHqiejz4fD83U3N+trlDeMdN+Gf97goIsjasNc4UI/zGWnG6+a+m6lnc5Xnlx5ZKCHWZJSvS4Kyx3hXguA5E1LigAwjvqXR71bdqU9Ohd+uMfMYzRTV4wucVkZXzBX7TghybWXh9BWcI6H4WUXxBhiRHMq8AdV1bojXG7U42F5eSn1d5PLPuQb3v3FXe26r1i7ziu/bNflyFiD8LmmjJjgqgAcIZcnHHajhZihDLwEL8TiiImoTw3TfEi4n/PhyETanv9QonRMQKo+w6Hfb53A9z9+hyWE24TrbBAD1+/QTopHVcQYoBUAxUJLf1loTc7r0DYUBX3AqFn/bzdCAtr4Ln4UFrOwhvHUOEqSmP6sKMKM+TKNJSyByZiFU7cV8HHGnjTgZUTEYulU+XKpV97/kAAKAA4AAAAgAwb/k4CAgICAgICAgICAgICAgICAgP+QAAoADwAAAaADBv+TwcpAhLCykskuz7Xkiak56aLo0xPtgID5JAVEHxKgalDcLM0AUOUVM4jR3fJfoT5iQR+SPsX6TFJGsqiLMRUOR9h0i5n+t6q5vJ/EAD1YeandKq3lk0XTwaOANgzDPb6JrbffaOga2S9+SZriTdH7g/oS7jWQFUCl4LbhlvZaid0HgMeLMIfDJ0+O3Ete2lXgv7kGLa6TdcB2icPAiy3QPpLnlN3gf6XOHpdJECElguReLBoRICmwG/1490a5uGxECHLTrt7u14J5NYuVmICxIK8m3Ldm6zvZm5MAZcirZW0fNUsoCOu3gPSvE8sdMBSJTYxlpjsR7wvQhhASinil/wRAW47MaYgU4DMj/ImwOvvqLXJoCV54G78ITmwKwdPxKAcY9ZRv4ElYDFKGLaCXN3RPiqFAwyvt7rl7Lfpu3v9/wCrQDMB+k2pjnop9Z5eH+cK1z3XjsFFt9j49SJlAfDBbIjhtnpgxmpEAT7ftR/FWWKqYidoCxdl5YXvqbQB/0HD60Hk90GxgQkrsyQpMKPUC/5AACgAQAAAI0wMG/5PD4NUh8GiQc6eA3NcSiQrW8tdjA+6/zQ9ajuFgLtrwm0wlZnVPQuY/VTawLGAEOGqJm1vO1VigMvhazoX0/2WyMVo1AzFWe09xWdkODYWUmVOyndvrTodOCE/NKCVU196UCq8QFsSQDjPLwmBBEUwkB1uIdghstIjq7VMmDC791emBp4v7Sd1tHc4UHesDxlBfusunrGWkv3yLaZD7inJr67YgMnBTsb4Tr3vUKnujm6nRX2kPCMeafl/KhgHWIrpRbuLBxesxpJPQpWl9WFU9lF05XUNhTkS1lC2XYu6ZL2fbzT7byHLCAyT/CRZ6c/j9gID62vPfTIB2d1Tv8RYiS5LdVSYwewVjy/TxibC+07+wj/k+ZDORSu8v+C3AVBjj7bv+m5X7VRTMy2iSKcnikz7FNCtYpg6l9Bjl/2tCXk5Kg4L2Q3h5mE4z7Cba3Thpkvn+bIMcsxYomhrZ0FM5k7ZEuhYZvv8h03fMXczB1qw/RoOWbTcjbsD197goc1v2oegr8zw3yTCvKApVkVACBwFQGTm9wFHNj3Yl15eOeZG0+BbMvXJO09USp3L+kFdQrumgcCJU0zNRyfqgqBihFrcPexJTdTzHbnDIVHFVBN0o4165R4aQ9U+AqaF0o/YEwgylsx5IR7+lG6MqGqoB7J1Yk0P5Urwew57X57+VdpB8yZ6EtdaZss4i0Jx6MrzpkMn8rspCBFG3DeIU/OLNFPLeuqu5AxJuAWY9W3/NgO7hFDB+wjayRy3vRMXn2N5cZGco7p2F7pTZhaizFJUnwqh5CQtnE2fa5uPdIZifKeHhIefvQvZwEyjJywAWmPgynCxuVHSFPcOEkz1F19CE1ng/fH0EWb9byT9xm1E6RPRdsqCtwIeWYBGg+PGB2CyeUIrP275dQyWtJapFSknHa6vAW5Ac2wAdGC5fX2kv/xMwnbmfFk97+f2Yrpr8Bhc78RloxCnoIHkltSrRQ0oI9OypNke9a2tAChVbHFR2oMRSCqGyNGNhKUQY2NqI4k+v82pM1Kf2M2J3gOMct40guObMXJ2B9bbaWiFEiDMv5zcjTug+KMBhvtm4QkrkxrfljWV1/gPrU1Qhax6qy6qPENB+2cWv8mxKbLFMKzWFAa7ePus4Pc8FePQCjLH4LH71t3HtncsEeLOG2TSTnD2tYC1H0n8l6A9oG5/q2qdQkaP8EpSzBktm4na3Il8t8agAxKrkJiZb8MpMpA3S+PksUtRLoqAkJkVtyeY3t5+Ta2h58mA6wM1AT6BpLM5d81DtSEyXSLT0KV5APIo2vSO6Y5cUqw+bqeaoDusALbLVogqTp8h8RcPq9Alatw1syo+qo+xrpIK5pPzq42fHOydc8Uf572t7NFYjNcbICpkNsuGI9O41ZGzr1zbvgegjvqj3J6oYVpSmzjb8vaLG9NK3zl/vgLwV11lBuHZxPuqyOCHdS0tb1lOlvuSk/IEE5L/ssJz0NYZTGcKpYHcX8hN81aDpQsTfWjz9wHdOgO1cAu5AledJ1CkYIiKCN0JdOYNSZc75c2/aGupje0TdaC/oHyS+sgVGPMQXX53M2F6iCueRAGfxcLLYomKeYUcUzTfjuZuI7MIEiMRjDDD28jD41+mMdF00USwlLY4r7OrzfafefjU9xAS/KrcPsoEobkCG3gmKc2BWhfiNOyVQqJeurBZW4E5Z+H6baN4kuFBaA1oPT4LB52xhtQwYI/Evx4kfGy4qL5j+pt1jbCZN9/MrOtTqpIDVMlXwvWxRKRjSUoggokHRFFajmWsIdKKu7jMDXGk/brPccv9kE36aNxHvW2CflpcFnIs9fuda9tGTSII5hMgJNPJH9Ytmxo5eNiptxHMtjRrrRxuF/w4zqBb4hwOA4VXqMzrT6WqKiGQxq1c08Rcj7uP9I8yCfP8ZrVHYq4DME5Bbi/Il3MnRa1UugecyBhd16UKsjO2Qp+PPWbjD9U8sNKV9QUECRso7LK5GSqI2zejh9SEaqFyX4Hd0w1/4jQ5GmVvh8Xvo6cjAHBbzRI+Q/l/kBOUMCajRFCQ0RGmmUTyNhNdXrTRMQernhDjwHGWpefSLkRb80c0Zzd2bFfk3bo3pEBSjLoWJnkab7YaS15g+sHu5KPd+PmY09rpID3C5EBoGLslmnv9C3rzUHh4V5UBLPoxxpF0wVB8AQTUpjDnO5GF/Otqi0zDhi9KUyyG6ML74Gp7s8uOKUlpyZpiDeLPBwl4m1g9aw5Z2VJ27+Et/IOszHIDr1p8qs2boUCnxu618OgIuOCm4k2Cuh3hGZwSCBmpdsjJ0aSRj4R2BCd7yQCOWGeD51QroJyh0tDP1OZgrZsgiyLXF4kgQY24v8S70xkKO1GDTvC+K/Giag6jzpLwGbvoSufdFrNWWh+L26vdtZu4WHuJsoXCAJltn6ZeRPRevnJtzi6krF6ivrWqS8Clf17YtjYB9fzY7OttCQ2uAif3kfXiagDiGZprpv04HKk1OT9GnLVBKkLKV9QsTTblU5QiP6BMMogJBU2EMEVhP3knPCNxRXt+g7ia2DvhWjf2HoFJ+hIZsQawqbBd7t5pbwDF76FuDpo9FO1X09BeiUEcobsBhm9jYxUcEKpKfFWZC3+mgVnbcQ94YcORCqLloQQ0E/wNu91wvADAUyF2uer9dtkTDwlJ0r8F8O6CV77/Rzvubt2QTzEGczux7702n26ejDwubyT3ivlszp8rvdOAHVb900HK/AnYCqRM6fcODVWe8rzlqwYZVyYH7OOQfKH2aBfEtxMihDedHREMcIQ9e4NeHqzvogpq1Gi6jUAUMA1PLMPsb505Pu0GxVWMzJ7JHjrDxa9neW4zDJ83wLj6U29besMvLOqGoqO6P43wyxPf9VtPX5YoSQk7WFezqK7q3/FFMoI97UNdOPVz/iqYDgi4u3mwEnF21eHIF+R2HBddEDLJsf1Z3Z075Gski8fDnuJHzI4L+ZCk1FBl8gzSWhP8Lex+0xM5B/5AACgARAAAJRQMG/5PD4NLh7QYfBdDDjxY0cO5ez4GDOfdsrKOj8qcFZC6rn1qyvYHReT784gU9qpb3Ene8u17vKFzwkq8tT0/S+dU69RFltaP3dClTYIzmxm44PJjeBS8SN9gLdUM0oefPRIiPxXH62kL8oiPZ+VMtmBCyaw28J8huECUC14A47PApoeG1K+IXeI2kbZxdKHQLO3AappYsEMx0gEUYlwUimCFZ4LhFaxJxNzT69ve39AmijFWeI9Z99NNJFUPqugVriOcKS4nPPUubeAqN/m0JbXCAgPrQ8+duYA/1Gecr1hoJ3Ulq9AmTVNqe1sQ+jrNzlHixFHHrfeK2vcb05GSO0s3hTQgDTWMrLPTewZ7zczWK3LHgKc6YjZUsIJXbEqJaLamkAVlOEa5hEiIUIl3QMQwsQ6X4pHYkzqJVDYJEt1s6exc8n/yqMNksSspv4LfPKe0MfpSRvQgeMuYDE8Xyh0QCKQe/4NiQ0B9GEyIImU+xPAd+VyjvsASmEyNRjEBoIy0gwAUteU8r3XEMfgbN6fjtKfex0rpoBJRkeh9CQiK2wygusG5JLvBJFOieXDJP0mhXrkdZrHByGk4BfpE6AdJFd1qmnk0pvpdzBUIOH8TJqqSOGsJnxSvqDHPRkkRrIrQkkGMF+CSZOQ/MaGgtPZDHAi57T3rVa6U2Y1D8O6sOhjQztm0Kaj0lshhwYrBgSca1ubMVquy26BoaRQa0uKbWfeacjABkPAJflXJf0MRaGkqCcw7IpHnFBfmgMItU8bOlQoFzTeZggO3ABgEza0Ra/R6/TQU+5DMMBa5dSHHpM3yykUAKajE5+jFChee4QW29JoMFv95uh2xgwY4PcJLab4cqe+1ChzgN93xUT7QpJWaHUbcmGcDtDA7VwFsAxDdkl/YPm3E6dWKBKzlC3prm6LnrRsDFzJu0R3NqQR01FM1bw5f/KZuGoXRWM8pUMFplw0x+0db/fu6T5jliLsczslQjYo7VbNnWpEAXOi8OSsYxxkTHlBqIDzIRyp7DKinrAcDhYHT4CUDWODIheBToucyAtt2x4al7B8VpoNY+M/WNyfl05ZyX9VBbSWion7JZpj+sfo5zPHlKq9szBKyXDU+tmt09TLIyCOlgcYKm9P8nW4wbJUnRzZuDDuei13s55bpWVBbWLrL+oh3c3bsLZbuUWMIYpoUNcU+49rrYHCvv1QpwyA8WXDxXk+lEt8nLprM9fKZA/m7zbWEMQw7Rt0Xr1VV8Hk7jqsk9KkzYnInoFrMlFy4oYWihe3FWQvokBqxrjeKxEPyU9+EoYJl/5psxAR9lkBmYPB7FwzLY3QY41fQYr866CCcWaRCN+mQM3jrJm+fGMcUkzXExL9Rcffz2Zwq1V4D8Ih12zXXvPncIKVUYIG0nFRbQkQco3AB4p1YAr6zbe7nzVOaZyiClNZgFdmb22LnFhadaFqbETDZ34nImSZ+5YjtOuZIURkQr3AKCqhr3HkCprehq9m7RTSJ82xIiYJR2uZ6dAwzrxcgsl/x4XKBvXtPI33E6J2txWAzx2PFNDFpMEoH3M7Vh1iOCNI5x73dViNT5le+nA6G/Q6KF6WolSLc1G7mNYhNY49NSr9PDYVp6GaBUKFK1g2k3nG5o7BIPOYtPEy9kgqbRj2Y0WJuURQExVNLuTVSyMayb+JIY99sUdnhjbcKIcypk/wLBIrtq359HjITNQVB1ig2iVjW0fWRWr+LdvqV55fzynzXA6yjYoVvRv5ahtRbNfqkHQzyJ+ByjRvfl1VNKR1xEwyGEkOkzQYGJvEeskF26Piix/Yjzfb65AZoM1xX/C97CMtPBgHxaBeaQp4DX3NKAqPHt0s9ch70BjPb/CtFaewd2IsYIWZJJntDsSb2zPSE0P+O1yOGmctQf8jcYxvlIDPUyIImafGiMdB2/9gA8xiJcwpfUTP8poDYhaLpBBk1BXUWohEBsegwmunz0ddeB+AXiJI+ljBmL/z197cdP92A0LJk7W0FlxCRSPX3vRMnzAFN22TMMvjFugLaE7EE0nWrL4MfpJRrTRnoqupIpoO0TFcyRAnWlatK0lK+WVVlk83zcBv1DDlot1wNwgcfrfRWlzGlOVjv/VAPULP0geuL2iqWOKQiS9F5jG46ip0cYWCByGLvy3X7hPoTHdSzC8K7OgPVHOhLdgfH03qy+WuaLDNgWWseLH+hZR0Q5jJOzGdmNFiytbkgY/1ANj8peQ8Q/mfXRFrFVweLiShe2TKJqjzvDNkmLzd80MgveOH/mgy5pLReuA9VSuHLPU3CjKrWB5zVFfjnn1ezPlgDqf5npSLfIGzELRi8ywDWnCXpv3sdQ2asGHwdphlW5Xo8437qE21ca3VKV3nGArbvhC+Vy4rPgTotaMSk75hXmFUlxEsIhPNSByc27OjAlSkMJkRndzHfQKWtW4CTMnl83SUkaX1rfV5lKYE7V+PkqL0vXphQXi6J7Ka8r37Io1+GGPjsOlKmAORi3RtObQM64MdW/q241dMu4BJGZU2voYs9LvKIFJ1ET43fMJkF7aTei9mUK99km0knR1EiJ4PgOzKIhgQ64L0yggfWOee7XZGGX4PD8MN8queECK9R3kTi2uUJ/x8gUfepAwtrbE9vBiJT60cBSwmsPxhQAn2fbbLQ76VB9uHvw3HIlR8tBi56svaY3gl18YXYgZ12ZiEVYyNEmuaAnu64HGt+eS/YLDaoSbNsb+7v5Axf3nvid2SBEImFIcJFk7m63JEHt2p7ICMS6Ckh2M8nHgauuQMtJhKMgv7ej640CH4kyoRP+BS9bpx5JIGcG+2y4RCp5sZuhQQOwsn6qCISaEDPSuv9WZpeXIgE8GxtmTD4sguAeieDtwehfQ2HymHsWdH9faEZykJ9o2LmBFoebTI3l/UqoAeEuacKgDHkAbmYOUkrXA9l/uiK6+Whad83a6iaogBFfOw9MVNs9oNfuDq+qnkIptHFWi2uxvPHP/uBEgCQAuFOpN4ZbGzjQG/b6S2vwpL3pb8r22ApX2P0hlF7V6HFPUnlkyX5zviS9SQpxmT14nvWYVpXWp4LsEI0RKpPh8WOFmacd7rRy9GSqDGOnzKnmXOVBjoRNabwDyMywv9ydYcbS9yEFLdP2kwMKpHHtTKIsiPeO/5AACgASAAADowMG/5PBzZw+ByHqwBEk87cvJuXq5raqFWncQ2dM3WgW358g5LI7rNbKpJoAvkAjasW4R/8r7gfPXl92xGqZe80RnaPjMIt64il9HJ3Cd6GwDSfsXyBoPLDF91nEvMvap3oWycCNkM97aaInUzoi1+UDDbZVC74NFt/ParWc0wSdZ7IOY9kBX4CA+UfWnwUohJHVWQx0G4qWzx8owgoGC+/JPDNE3b3LVyJgeLLvNgVJE8OE6xKu776nr0XzQ5R+7vrq/A358d76p/k1kV+lDR9dRO+SCNFo/wuIZsFyspqoA8Vqw5nszhHL6raixfh/jG9LF+zTLJeKEO3nmcKEVyxAVt033EgQWpDRCf2mmYVH7m53nUvOHE3EldHZDFHl7byISwF+QmZaCpOSqOKvYpyfNeelyVQjNRIqnWKtqIEHWB9/505pCARl4Cn6Ocsgh6eMC7S4wWiP9PD4el/wgIDKzJEQ3Z9py9OdKfQM0/0axruXQKamLgCYDc+zTAfExT4Q8ZQSrZV0BNRzXVamGgB9hhgrM8BxUBxkBygAja8s/iI/Rv0jYsy48lFP06cS8dLLfMTABKU8+dHiDwim3qQkmFOApgBPO4sbfSKMHPuoBATdDU8twD/xDufGpsTUCxDxZ8VPA587YbYJYsU0DkrZk5YcqouonMzcDEmnIcc4/DumxijvfVQrVsY0oCY18YFXKldDNwY8obkTVycHJFCXc3QQLva5NvW/uhjJIHUWNY0XVdH2+2iAmKBNKHfg2hq0A4XugznjtKWCwnuoFvDJHJXUoPnbI2ja+pS6wjA8R9lidrYuXeyv3S7eWThctOwiih+bSiKfUzWfhzYadJCBg6ffFDhWZxsiD2mIsO3UqEbALTgE4BPU/aLrHXQsSnJGGMDHm/CRkinMySbrbZMK9LNZ40Cx+ohDpi2OQioOTxdkxQgqESXy2hqs21qb2ZlI6OiZZGCLCAjP2jRmgr80MgMLsVu/S2u/7Q5jppYVh/klW4LUUvciumQK7VYUvj85KVud4jIeXPTMZGx6Q5Xq09KM/HX/Y409ptxNMv9Xf4rcsbWjrRaLSj2LEoO2sqDU51p5x0gtyHKn0hZd6lxNmMA+6QXjmJAO1oCXQH9FAiAvTcIZo3BJdVdo6y3PO6VnIUYii6wzS3pXHR/Bo1ijfAFKY6MkO8EHICjINyJBSryJDPQ+nmB8+T/alzKOiVuXfCJPZbZpav+QAAoAEwAAACADBv+TgICAgICAgICAgICAgICAgICA/5AACgAUAAAAIAMG/5OAgICAgICAgICAgICAgICAgID/kAAKABUAAAAgAwb/k4CAgICAgICAgICAgICAgICAgP+QAAoAFgAAACkDBv+TgICAgICAgICAgICAgICAoAnAxVrGEzox14CA/5AACgAXAAAAIAMG/5OAgICAgICAgICAgICAgICAgID/kAAKABgAAAAgAwb/k4CAgICAgICAgICAgICAgICAgP+QAAoAAAAAAHYEBv+TgICAxB6NEH4B4gak45PQy5jd44B2VrDkr+F3SyWzHl7tRICAjqAzxwqVj5qCNLfPr9lLsON8V0mBtBxoKJXW82YtRgQBImLS94EWN/a32tmce4CAgACAgIAI4F9vMRSE+NyAgIAAgID/kAAKAAEAAAnLBAb/k6i1T8C31QDipmQ9Tc2OvHBXplD133SIzcp9eKrXw7k6x1oLy8P7N5O0tJdMzWJQwn0kN+f9CId7nDtzjzUSJi+C1jT0PygBJ4EwrjfWG9lgPkXW7y5rZZekgIDIPwa37rT4DPtQ+Cr0HqPuze6q3ANS+vdDpAD1NkngTCdNyyKAUbk7GYoXG87XB3QOZi3lKOc52ny6xJYPhfQLnLcM3EyKJ7n0UCwfXgYOP/lCuyts4nNF9W92whx8YQ47eTGHJ6VCbzCHCDhnlamBtI8MPe2GRf9erYMaMb5Nzfoix7Fu3nRl+n6vBMMEncX5kOWasK7shew/lyf1EBBpNwMGdG81Xg8Y4swXibdx3sl8aGTZrXLw495tGFzZnoYyl4RXPipXCMqdB8kEbJ6XLx2E5a9NIiF6T8RI6N/Uw7dhRb+Oa78u/OoxB6mJ5ZjXdkgRrdM4HVJyC+8dRWe3MfrGogR0VAl2kGIHTESCSiMHqJe8BtUrlfj9+N2RBjlhRm+UB0yodLtYqsOYp3jsbBF9Mahh9XmfpGvESWaVFxPWLqyxTAuojSzWs0Fpxm3o2kxR+tT84iDgSf72ogHzUq2A/zIXUL7u1uklbmf+cvLkPKUpmzpSvNCdSuCBZGSFAik34eRB3uMh5/oBWKCIjP0uA9i9ZjlFpyZKI8XkVCeeopu/gWMRlG4K1AOI4h+mEhg1T9JR1ahndjP2Ze45Xg80PkBu8Tjo3zB80WAdPDOOuhtlGhh7CIlJ6QZSftINbR6WIi8a5hw+owziRBbPoKtbCtSf5g6+m2vY7PxQGmoq+hZC1Un0HLoXNxO8Y8J1cYEcuXowKHqSbXWvkIDD0wFIiN1YzrDCZ7LrEqbnRw+EIkVL5jJjiDluu84V7pN2o0o79n4H/WX3eRdtR4XUaP624YCAriZvmikXJZFxfLFy0OKoA0BwnFYAW6UzdW9FQ6wXjNiVtaXHa4SADXFqtp+qzEObxv6dlwiSfWYN8DuU/xyEsc2vsVvpiUCgBtj7B3FFitpRDZN1yYqAMLwkQeNrBs2wI+I2zgIXsGRmyZvtFy2/6TaxdSrJz5dZ3ajASgARrcWpRsg1Jay13SadXOXD/zCrsfs1XBba1H05WVcN/TFoeK3S2bmy3ea0Q/E8VucLyzE6qMkEUk37+Bz7pVOLG6JI5V+lfrX1YbIn9k9rP0h54g7e8C1I++1i8Rpzt93aPYtpbyPxRjB+M53gVBG/pFNJ8zqXtHNvzqFmF3bVotOGmnE5U2TZSYzn2W/OAYi8hfiPcypKvz5EWid4L0tBOb1pEktyKMg2H061/siTZDpmUQ3Q1Vq3Zp09WY4pVQ8811HA9GoSg3m/XP0UPhhkYrCsf3mXsmhXVYru9Do7n1zjXzbzOmXh0q7ipHRItL4uxkawLPGJMyZlbUXXAFVT/Akqfv89Dh5q6FfUuA3d4NEjLpdMN8BzK8uowdNSIaLuCmovz1BgZsd69vxwEw4IJ+WgQn7lIy+kUdqvIoLtyI3u20D45YTDgkCGX3XZqB5Qe0Dit0vbtjBihym34vyD6CFERCEdCPqG3g79w4NnQk3j8V6M0SB9bOtC6RHcA+EhVeDEDw4A2w9Vq8ysVKW0IJUAv6HhzU6uBzdHfdaMTmMkCXgtsNtGKjRdJO8FZT0nX/Bw2JMtMj3/b63iPvPYSVWiMyWetFhN3VevwAQ43BX3oS1LPGn/WOEPNUCt70LUMUa09I66/gF2MEu2NH/ED2oRwN1hlhoySoO5TkFyEVjU48L38bXAhnm5DXIXVwURQs7F0WC+VFFotAENU94BlWFhi0TwCnDuy3dgvg1VJEz+HNj+/rGZLsNWFeOLndSgDuqFXFWZvde5DN/WbhQ9FrVVsxF+MyFfRapqj3j+AuqxhmdSmskI0UUTKmavSZ2+H8sPxBSF3059rnRhXYAJfQvGQf8dGk1kazc7vXbpHPSrZn98dQ8cqttWSFrkGV5hwV3ko0Yni09dCM7GQ0lBjvPR8FQwPkOthrAkKccWtDvD4jebWlenVROMId60L4yMFpLNphEljmZrY0Ly/RJafIA8ZjbIUk9okJnKUAbhIVOS+Vri4V1uYg2KpGDKrJKF1C4+MKoHIu00ZWlDI372VeDqgHwzbaJQnx1Vy9SxWmp9gHQeEY2i+lwRmRPERVMnK/upvJkQWxtjGwyX2XLGTeZIkmi6mn3UuA6hktUFw0RDc2RmS1W4hS4if8KebybgmnBxJinolasdrQ6wRox2bmeaq8YFpiiKY6txxaTRf8drOCNILlOHsrqLQJeWKDUtnvnVMaW380VFib38/uIpcYTrLmdrTapoDKTZQcDCamzB3sN4f6E91Lmndn3ujY9RwQhbkCTIeccgnGJun0NcIHc6kFEdjBwjFoBUGZhDvY1elXLVUkB5ofNSZpvJvpTQ4F3JcWczsO5NvgS8LEk815aLYN1uBVUGgsKbS+xeYcJhQX0fXhOwz7l/6sPollFkqc0SpQpIvQEAu/5GAYW9PqjBiPeXzjhyKY1YabcA8n39mXiJjWs+InKAbxeRgVy6SsJ8TqhFYEulNXR6p3LxeyI0FqJx764JQLq1fq2E036h4YTXIoXY+/luJbe8cXoNqkfErKBPBiPHGUbWiHBaxvncZzbOM7SmWOyyiVCAjEzSTTHVNs0hHEIIbZ4d31n3ay2YTEiO4+JNEwA4VpNJNw+ejyDKjGcIcmkCyXrIb49Ovkwp7XUDpu4BX22NvWtzw3Rioq+QqicgOvmSTGqBmtRewgc9v/8bNKZji1MOWzyDHL8xIsOAAOBDTkE/QvzQoFk/agX/hoq/us07RhNUgu1clUDGKNKzyxA0oJ/dJnEujQtwy/lP4GGuUCiNJRsEOX3lp3R66+ifWAotdxbo1NgqZBoNG5JnoogOwJvrZLGwd1dsUH3CiTgiLoWaUC70svMRFF1BwWn2+39b36rXxNucFbPbWoCe02l+ZL5gOLuK3Q7RQpVlpQBthw0IJG/62oFGOUDK4V3k9DkiZoZYiWf17yNvPKddzQQ0OP93JziWWbABNoU2rPtX57+B/0epVJOcM9xPxNAeBvYFJJ+WTcHZAsI4LCAm7DtuDxN66+EAGPBAwSN45UESKlP1juGYrSkOTavDtpyLYSIRTagMljG4UL2Ogh4IaslQqnAzmNMF1qKevjtb9Rr/NJw3thSawnMaqsNDaxHWtl+NpAGUgOv4ohjWihY8rQhu4cexpiT27swlvFfhuZQvyEkA4oDW2PdTmqEllZ05RK5qesYVgYg6A8URAcqcTjDit1LtLA9qzHOlwqe5yqGU7afmyv+QAAoAAgAADUEEBv+TxDaOPmlx1zSgsMSgv35Z0z8BEByBad+PH2/igayai4dLx9eq5IgfpkrWmkwqXPKCu5ogmIUGhJ4wtKoaDh6t3uQmFV6kdATjAES85MjP7x+QalyfRVlFJ8IET9uV4QjWKTHa1NDRUICAn4du+GqPgqpxPujvOuF5++Aj8Oh/h6GAtXzPHrTfOpu0R3jBPxI7wGQPIt+HV1DILsHF0UBEnHyZ4iT5fvnTZINb3RtGoNnt1G5SJzo1uw9+i6Kl+bEHjGeKn6nQxAhKz/cIgsNLgR1KtdAYsNpbo+E3rVtB9QyclEw9/2ctnopyoxPksA+SJDnPEtvHsbFLrRo/YAY8jmteh26wQ9PJqASIGAKVt8PHuKPqGv5EhPrKIZjM3FyXp7vr8irXe2j2BmvCeYC5U3ulaTaOGsM0MWpkyixi1xY2gsj1OrhvJhiKfG8XAo9NzZ0XRY1/8jE5kmmb7XiKcc1LNlmFObhdr0Yah6pnCPO1HJUxigh10Q0MbBFNGF2pe0QVL9YuHnq9JhoFwt55Xr6GemA+Gr9ApK42ayHuuToLRqiZ7ifpg6RpG2sx8PndQHqM2Li+q4+1G7mr98pHnmvhjWMsRum0Q1Y5EYhj64OWzw/bOnJiAW+ak27H5fUt+i2yfzCZuFLJiTivsNqL6R0TA5A+uivjK3H2AagTlcwoBYuT8SCSaFdF8XI9qpcMpxLbcMggFMVhOPPfNT3xssfgu11VYwjYmcqeT7zVntmr2+8kMvyuMtGtunKM3UQHfzERpbXDrdL8kt4NiV3iDGffCv1BSjd3yn0jD/13ScTYVL62z3bElw2Il22kqVPjXMnE3Mwe02MQhuH3uwkE8YBvP52DrLwqInJQEr1pIlngpq1xLGt04HGEwNPJAPMl2x0VgJ/PEGTLA+VkLOqN7kn4R+WM6qKtMSadXpTjBskydn66h7ghhNbMFidT4bpcwMYqY036CWkG7ALV/0C36w+zeF+XLGucyj82XeGc2K8dQGteiGd19parwiqDAGKP3TJ86dEQAPWCbwhUkW24X47q+lZT2uf2abhSpYyG3hJqwngGYiaBFaXcq2U97iUz/254uhDqPsFI2N0lbSZLgP4FLwjxZjhrIq1wR7Ab8o76nvMRwIkpChGsZBur9TFq5uXddjBngy10ucFwzjboc1RVpXJntCG46SQvjUUbCzJFXiSsKiIVSdwrk/BDF1xNRE3us/GK/ukyl9VKF+LxXQxqFS0vt0NjEJJQkzX5j0rBlecWVkjN37gBe72ZsHpQAOKEXKkS/J0lFavIPR0g8MC2kNA0eO2Xt/f3+7GHzjHLhinEcLDZSRyr5VDinkp9djb42qd0lDduI1Zuvtg91FQQwewmeBR2zvP5mWHi98eNZAn1SNgSjrp16CWORx0TxJD1cihttWmzygLfwF61K+f05qN+wt9PnrAByBfs6bGGvuofhpCWh7CfD4eY1sDQ/MQRHQyCzLrlPfCuHO7r4OngK/ZNAyXTd2lbq5eB4q83P4wJWGpBzRiRTUiTz7GGugy+rHSKlfb29RyANm+EbSzE9Fhd/L7FEn03OBe+GmHBIzYQa9IVYpDOooD+Nib5/hog/0T5B0wBYI7crp0sfjO2X9KRSSFxeIKeZy6X9sQ066X8xj2JqYyudWPG6+bT9Kx8MJS3ay6hvh1n6dOyM76lP7/Upf8QxTaGndmFPFFi7Bp+AzP5RMBbPqaSsuXLhU5rqk6uC4FllBReb6wycEf3HNAl7fnXFfEPLOjd/tIlQH7BXBR0DIabQ2TceZqsM9g3ajeGA/CWbZdCZ/KioKtTGz8jPOhn39+m/AexlvIuc6sOEG75ifsAdwz/a27IHukOV129AJyeDw/Ysy7zhmVKBcnkYPF4d9eGbkSfnRqQ2IIhmKejA2DwTdhFa9sa/UdFNMhROn97ilzc+qb5r61OMKd5br1p8kQmioCAqM5nqCnlhcZbJrC60RwCrK5MejCF9rUpuCKTdPKvi22KaYxPwpT3CuhEssEzbThcpy1Js4nZcUofuu8KMXJ7Usr7x3+Z6r9J0JXTljMdXQ7wF/OcUJhDDLdwaQh956qIzHI5ZDQev0n+zRZQp74IwInoJoKgIAQEq3mMsHNFc4jBQ71JYTr/ZwjED0LIfJit+My2ZHpy+yGInkbox0i0kIE9o2eTO/YPTZ40OGKr6Y5KT++jYcqglzXP5nDqR4S21scr6NfNMIYXlKO+Qklm0/p4gabd3UJT3snS3+CRaA/zUVgBfbcrBOd3w1s4DnJytsP9JWOEpHZEtGi2TPadbyNB2cOblx0AgblnDpfcDNV2Zj4gzPSIrXW5Xy//RScPH9bJe4iAv/0FpTWkBnzMqRvZvlHl7bz1sc3XbGMlHZ6/7RlBLxiStt0cPNBcC6BRHYrUCQG6MLDaIcseYy593xSxjOEtPkRR9zrzV/afo5YuMQlS+fqmBnNHsasKhf1xO7bRRNBTwgxKByyoMWOVwJAZwGh4b+55a2qC0M4m+3ixtqXuEb0M2YqxNLQSptTgLBubN8JDq5tynQpnkKOwaJJ2YsD8+zHPszSeDjbWw4zlIXalrmHXuTQoRz9dYDa9BjRta7o5KuhKYhuNx1tbNJDk+Z+s4Iy2I/oqerSbN1+jn0W8wAPV/LdXhzNtJkdURG98lX8aP7xlRHQDjM6m8IEVXlfrmx7ZM1f/aJW/hXaNVS6pgGg3oI3nHizsL82sk/JE0qXvhCHJoLb9KRDR4R4qDGDj7RYScEdYz6mLLmEoEjvCg6w0DnokH4fxKGBiSrAhS6k/9h66PHb9cjM8rFZHp+IUNK/OltiveOZTKC+X8b0rA1gsUneBqaDQcqYCBoeQuvtlFytf+KqMhgQLV5nPyN4tZg5H/H3Fcs75SV33ZAh08+82hscZRFaeaxpMvSn4CLT+1RTvCL4GGW2scClIsje/60F7SPLv6mNjYfSrmqaqzK2iTmu6mxb5dPcnruVM2SA6ATHWTkYNMnT69Id7V4u16bzkx1Fo6bOxNM7tNdDt63WSYVvJYME2B4o45VkitkBGtTb5IobXIqKkyoyBX0DVl1nOwxt9WGFWF7daNUG96R4NvC+CCbIZDeXkgi4lgO224YKYls9aqd4nKMk6SRR9/QhJ8cDu/WNqE+rJPiPKLqo2/WZcBXtHgS+qOBxZKhcS7XAnFT4fYiyccwEyAz6AgUFd216oqk6eEQ+YdmZ3EX/kaW8SQ8XA4Wi0plV8UwVgqvBUiUeg0a7HlYa27Ev4wbv2M26OIBIpJondheGcqtS/PKV7Ma77dYDdN2UAjIvDyOFBrfX4nLbmSqNV2SXOJVol4KqI1aJmeBXA575pxCQztPH+AsvVTAUUc0iuZcKjemwDpgIVR0UwAikDGu7JoS7mESI5ihkqzpqCcqTlR1AYYACAc4HlO7uulOpa8GUCultlvk+bzsVsJZU8CBfI0CEl1j4a01BVkNfNYjlvSe4OScpCUPHig8QbxUj0HA/TuFny4QWC39Y9NV/AucC+eCbQTYdfaM3mo+GYFA3kIsi2bw0kw6ML4pBJJhIStMPPwj3uhye9KlLqXcc1xYalYj4MVBYssDnGZ9xxpwBQPc4YAfJFCaxEzH/e2hDuPZKPgtRBwEHAAKGyqQ0uDZ4rBySpzKQLlJnZ+Lf+1BeAiJNtmbLLLd+jmp0+KDF1SjrlWftP0jWMLKWz40+6aA3HXZ5SCYvl6tDUz/OwVnyOpPqCPrDxvruni6Vhdosj2rPMC2Kp3aHQf2HVWlynEr8uxPfuhagaXwlWEMgqmX9hh5dTu2Ilg96ddOBaYOFtD9UPtrjwIr1r0SrgQKUC67YvduOtML+n3s7rcmt0bAXNf3BcurPZPs3KkZXw9xbL+QBEJyO11A5bqNsQDEEQsfq7TfWm8KwGA+2BQ3pwYI+kXgJi9yQdLs6J4/8nXXEc5rqe0Ek8kf5YIcqOCk10cD9jekaAtlnmbmy8qvmCbV5xYkq85LG+/TYuhddpNxNMLsVwMs1q99UMg8H2IFNQBQiRZkoao6y72WWgX8rdMioUJoE/46cp435PCql7n2AGstHdACEfF097NciI5wt5VVAqHyf/CTU53LTxQDGk9C/HTlw4G6lRUBDDxQqn+QNdp9riooyAAJHL3E/Zpx+NSahDPkh5CdDr3cxtePHSTTDXeLH39U2qVLdyOcdQbfCCvrFxEqnB1M4opMIlMEQ2jJg9+OVZ5gK8f9fDP/zeRXg8jVDoc/y1Og4Eg83N5MIOL2wCzB/qj0u9vmCU0bYgGB4Cyor4cNtu9QjXVnZBbrJHyvTDaLH9vs2rDnfWrE/jaGdJk6OzHpVgu6nHgH/fGZGnmjkynqbfwyaLTUk9Vbj5YccAfPgoHZksp4UsB4WjrhxgrTCHolUzVvbXwKMwEBtI2cIO+nXS5On/Ed6yvuCANterpWhH8Ok7L1gs+8h+VZioxRzb2fK+hkJFWUa1szWFmxf40H/GVK/k74gZGdG3Nv+QAAoAAwAAAbEEBv+TgICAyD15IfjZJB7XgN3w3ZrMadR4EcHwWPbJn8wZ9wsY1PqhJ018fK57Z8y00ySdEUUeBgynFHC2p7ycuoMW3tD2oCSvIK4Fo1eSCTOvDywnXWC/6YIooMuQQymBzLSIfAy4K998+nfeyRXK5DYnXp6EQhWWB8YysiSeYu2srF5lNk6Kepk4tOsFhfmAgJzQAM8ApvuGTE/buMYZsVE70knT1hVbCc//ZWxCtnePPPBzNqA8ky1HmxHtLOzdKP9B9w15CEFLrLZUpS5YI2T7ML6AgIFeJwBxUmn4tc5+7XPGSp6DyLEZhb4S/lgMdscu/OzyZICAkwLSi1rbdHynToml7WxLc+PrJziZMpMNCgQgNGe2Y2W0xMZioEEagUxY0lNq6xW3p7vJVBlQS9oSQb2TWSZsaP9wDWbvjvMrZn3yNZZxasKoFUDBHTlbBQsL7I2Ig3uhxhVYGqj5u2iqOyFTYjvkgICRQhgwkDK+YlPu3oapKX2A4DqzRtjtF0yH9oWKnWeav8PtIpRDFFDEo6xb9POSA8UA1VGVZiJ3HVJ1Z4D/kAAKAAQAAAAgBAb/k4CAgICAgICAgICAgICAgICAgP+QAAoABQAABCAEBv+TgICA0LuhSu/DoSgbWIlLpTdh8cnRA+tTf/xRuIQYhQFt40mzld7UqjfTW8lDGKep27Lp5R8CzDe53rxcAHeMMzaH4pNx2tHqHQNyUWxZXMPFa07Y/MrcSLMzTc/Eo/8Arc4L2qGk3+wUZ+Y92LclbkBWQQytkLM1Y0Cto5nkQYTIZU3dgYaL+Cr9fz03C1vqPVn6YWWMyhkCdsQB2N4gewNw8ZebVeuG4sVCh08XOhaDkB6Jm4CAuU3Gc0AcSucA/sMkWMQZajZNNy1VgPm87VGMNgWeCjWR4sxvbqLncF3d8EdZAiNk17JD+o/n+FvlXeeXcux8z3dFx91hVTfz0ZwTB45peJZpuk2YIxPROvsGS3FmjPl41qpDETsZZ2faRKKBcxbjs9kvFlyclAWOc9cCRgtllkqo5AJ9Nstn3yYwOOUOLkeUuP3ye2LKlsGsDLYMq1wz+IiMmOFADtB6peaewQ+qfb5n/udc3IN928Qjvgqq2mSsmEXoFJRbunNMGkDfAvalbndpt6HvO3WAP48qKS/6yvYwlTFZU7tpQqicigSS+qCav7jdcNEDGD8nyGPHs0/edVT7jaDGjByE5dQBkK61sRgroAdzJYCAqnYuawzA9f5q6JhxAdhmXQALbYKfVKHx/WfSF4idCM8G1oKNIOQ6fgVjxFoyw1ZqKxUrKmZcUU+ay/3wKVA5NQ8/WDq8dYm7zbCwiu6D2z7xUCunUD3M33F48/T12pPd8PEBOZZjPAEWI9TK+Ou/Gg/1gegfyQPvGQhDJnnlOzAcVaCb01LHwllyYY9Rj6M64XFESUjW/36brub1UzF81azVg4iaF/1pb150Vfi75cskmD3uQ2Z6ZHfWiF1BuMnDUH6GCjJBr9ApqbxuXHo2AxYm6yb2hYCAjDagPLAzBXhpsKJNUvOdT1p62SNan4F+Yf9WYl1Eq1nYN7JLyq9TL24IIpbd4Y98bHw3Y1hKSpffx/ihEaiCySALZ23ut5ny2imnkaSDZFjp63f9seKUscq2mlr5jj6Psp+xJ5SYFI/Sm4dBf41B8hF2WP8cbnsxLGUlMU9AB+doRrhyLjBcNOMGATdAWIqsy6f6UxDEB5dA0l8fNIhgacSbd22OSdXPRBKJ58eYwXaZaTqG6WqAsIAFSADcIm56leJBgRhWpdy6h79TRV2aXIZWR9uJFoWQ2y+qHenS5my9G8yv13X1Mpmmlun5A/qJ81OKUI4bqcfWB+VuE9t+ZYu0ACJksP05h0oVhRkMKr/1U7g1v5YGln8dG1NsX8PGfGMhuT4h6cibObJf33NFW9jGYJNJio4slHDOH1/Rs3S8MVd1APdC6+jewHNQzMEIvrx2d+gJ3FOIVScMBk4lPEfP8g2etSVHs2hMgP+QAAoABgAADG8EBv+TgICA4PdR90H7rKqcJad1ucvfhtcHtB697WepAHchHNgiMwDxgrL5FurC8Nlckv71VtSYyAFmiICy5K7Htuf+d3+vMIXEbIVwcVPvWJ22RDplNcwVTv812raefE9airVmPxDoqGst3cSe6x05CRTHI6+hua6Fz2KbxAVVCn1VLz+XzXURVYnfqC7YoU+FZL6fBC103Sml7umK2zKL8uNPW+3u+CUaZVvJEOCXU8jbJzqmHmqYB/myqS7eN+grosih0At+siFXpDzdoCJI3/xyMABEsPF5ZsZx2YoaxpSoBXSShauZXp1xT6sFdqRI4yePBk54juMc89oSef4MR55txwSDRIvtXNgK6oatLQaplRceRBFlxF6Pir6tLy3nfq8BLRjRUsxrLQRKvJQzZr9gypdvIgyBv/6NnauMMGsb8mNIUIDXBYVQBgmwRu2A4KS5Kd/omaBWahvBYLhPltMHa0CJmWuPy7ZnWYZBKqb7q/uAStCs0nzqgT/+MPLUqRGho09zGn60QH+z2aU0fVJPZdpQVCiBB/2DD945+dm4KXC6jhRyQhhG8iPzemlqoCjBOn1ZkuPiIenRhuqJ1RcFnmHnDsJcVhMA9x+gacm+Mbp/RYA3UKDJ/L2oCtcLMFse/zi6+w7nH2PRnL8/2DtnjpwNSeQ5uJmzFAh/NBIIo+7MQvEdzHubgfCU4f89i4qyuUOSKTxn8fJb93vuZnjLNaenXrbHdhQSoXB0+9qyVEbEa2MORzeX5Qr3yFqQWrbTF8o7IDoVaCDcBYpZxm6wBImwnwTiA5eTuL8glZDiw3WAl8UGBZGAgPLtyuYkjcUc3ta5sfL3m85reogEcZ0mzFyxl1CjsYfZSOgqE2GkHJT5X33OfBukgYl07crPrc7pB1MQSWqcJOn8HOOxZ9C6O69JeEk94H0Uahp8eFU/04z2jSnAkG8Oe7haRsxYQHxBBdcjULbAYAmT62FRTUHFQFwPPZpyaQssMvcwyFYIuFyD1RITc7udCoPX37uqqugOHoo2Drxv6uXpOMrYCo/wWSFwAYEgNZxf96qBQp6Rhh0GJlp5nRdbPjviPmWg/Gr8YSaOPnfyltJsCVlc7juZxNrLYxgBlCp5h2BxibVtDA/BjV3nsH/OU3WJEbgjrnNXCxwsinqnVZhJ3m++1tnvCzxRvueeBD/aAHnKm+0z8RK2I8V67jljOT0jiKskkinffWdpHahuSRHBzlKng4W9np+IKwfcu1/wwg6TS0XLWgMBlTECFOFDAQQtrn6muhdTYwr1XI41q6pCH0NC1qq0F9bmrrhc9LOXSBAHxHwzafRDTtV2lz1vg016ilxw7M4zzylEwwH0JEsjo2wn4ve6rXNTdRM5SiJUZQro87yTIe5B3EOfFZYfxRyiE+Pycns0oN3CFGZQoXC//eB54v9v+gW2eRk2jVY2U/d4iy8zjnH9ry5/3VmZ0yPsVgvbn9JH9dPvyCtWc8aem42I1JyPZ/P63v8M9S2SYIA1Zz/QMnufrpFuBFkswJwBFLaYpAfl2prT0qFmymYvQTLqv5bQkggXrf59zex3V9wLq2a+miAy8iHH55qToKPUrft2vLJ+mQKfYkDGtot1sdOHRVknEgK2q3EgOCe1JjOKMousMqfyrSKkYHjQLq+Fqb+eurDoSB6LYv4PBDqlPFZ8/FpQTkfKRSoy7kiJk+tj6hii8PvOKqjgPkhvhpqj5sRjQZtwF2/IlbBsIELsXOpCMwTJjeLhmeX1mMbu+QdmkyEwhOMdxj0I6nJuKfH+3aRyiICA1eVZ5dgVKtUASwUhKD/hWRIvPvFcnH7X2WjgVBGu7fr5Prfe365qEybrMeca4DOp/xKQygotOym918RX1emXEaIKO9b3BTB7TEh26N3FqMrTo1xaAG2hTfwT9HNL7OoWl3ZM8qlaiJp4wvPXl2rP6bBHSz9KHytuzjYxF0YKUWG7NcdATubPMFeEl3QIk69CDV9PbOJO9tN7bRIAZ09eq/w0mVPOnXIEDzur3ulylL0j/MKY61Xxnbi74rqr3YpwSWRGkNkJZfVbhHT3Ippj/1Yf407GIFKxya9tKbjt08H1ePuLuO7u1OFGDGYPzX4OcrfqQQg3BUjeP7e4+QKtAaqtT0jvk2kM2i8o6tCcEc1K0JVKbXqpoLY41jYTaxlPfPdES+l0cz7QqQopCWppRbALDe+Q4msdojJPShD37ma9l6IlNafi5AAxrXMayg/X6PVWEyMZhA5FBj45wUB0WHkExa43JFMPF8oSGicb89AbaFAakDgb1+zjeMZ5Xu0nRJlm+BLSLF2xf70OVE98iOsmebzGRVSLgKqyFy3UfMM42i2lMkjZ0EKtX/OMW9OGfo0nKtKRsg3HIELtFNZDWE8CntmbH+/OFCvGL7ZY0Rcze15Xm0OHvAmmh37QwgpAPSSOagZySJQ7B0r+raic6uw0JI2oJKEspnPH7+seUWsuFNcxNNusAyfylqplI5EYTa4i+bltnjYnhtARtJnFAipFvqAcH8H6RGJCwmDWo+uGfFLFq5ROSxUSm1DsZ6/o2+7JjduSx3c3qvnYPhLnYrq+QqzyHSyquG28i0vN8dIpPtJN1C4yDd0kIrcNYeDmHPdP/vYB5+EvX8PtMuVjAPSIsl2f3ekevEL7MK4sdEre1iIT0qo13D9BTZvVRz17heSJ0om8LYlRFUXfjK9xMrB1n4COaT1A7SmuRN9ONLI0K8oGg8EyAp5puBstMJXW6o8+xSE7Jxt4MJE6JZBHGYTDnU1+kGRXG0d+YMITSK7XHHtopC0icX5bECa+f0fPv8uCL8NOCSKNYBSBQE9StHuDngAz71ACpEy6O7um7pv5+Gp1gGLbtThoWcquAOLwKImMJJK0BumpYYsRZ0MNL5euKc/gTBUPy2TGJ1ORe56SOl8cvR0J/b40q7WswN3SpGbJm678R5/nyfbiCbMQEC8pqE9OcTvCG+7rACWbE1ONyp+zCL2UDpVE3ZCY+XmQ0lKYGZ7iysZvezk5glGEps90b8+GWKiha7M4r42j5RH/A7I9oZmu7D3PelRk7ZPWmTDRCzqAmvBdwWY2336QXIzCn0spyZ2N4C4owU9yMmIc0XBvcDpMnAZlbvaFufmgzDzYKA8svNMjAIuUVBBT17NbZZRp2GbeG6oI/Gd/skYxItybjA04jf7pP0SFRZceXwnHbE4t+z6FJlh66Ynr4lFs+gCxyn9wxELdikCUoEvBNNzDzO/r0wqRRYhOP6JAawTC263PX+jhkcdcqYjJtlk04eg3Z39Bcx/pwEN+NQLmpDuZe319LF+31LNx/hYAsC13rtXmrSdjac2hi86TkzthBAoeKi1NJlaW6hKfCGidq8A9voopxrqnIL3rXpel3ySXlXWuLQtpFfBmruHu0B5dFAc4Vf9SPGfrYcPVbCRk+Tiq7uUZtD8Dr8J582c+W/WqTpq/mgeK/1sqf7BeR8TYdYgCr2EOGGEsuFRz2QzzBF3P9wrnmrAgh8yLsptHmKuQAk2fJLKBvnDZktld13oluCXqwuZDSW5OnFtK059IP1Rv1ObV5LNzFPz/f3+ZO56ubZ5bl+sBIpslEC//F72vfXUkq/QNg7TzGYm41WwefbAfI8VZN7zV3u/yavT8tn/3gJLa9IYOT7T5kgv7Rai/wJeEuE5amkGnv0//ZPSwK9zOu7sRL20pzYm5lC2uRGqFkEZSGyOZI+QKjzkJPdPDuESWvydrVLWOAWkNNmAnwSoQMX1iR46Z8pqM2La806rFNgoWE8BlTDQnoWxDQiY2bpFvVMO0tkOp9ge+HeHtT/NH/Y0I6Vm8wfw6qFjKeZkKOBI2td+aShlvKr+yMIHdE/eZCdA7dOLAS2SON4xgMkaJl0rsGCoLPQnJJ/y76tn6f8UKg0tAowZRAh6SIilpwf9bcCoA0XS2SG5PHpmxg5lcAfvas/4hPfygzg5a2SI9NLw7+ukrECcK7qJkgtQNV+6M2Aw4ifvS6kKR+UH4ELmGMK2keQbtwleJXs+oJp6zVlzqBLaeObCu2Bv/BkMAHDQvxwJo+Tfw5g4KcKFA9oXNr1YMNhR61ufptFm9iPHnBd3UEl0XhZ1rpuEKQTnODfzbo1WGk2aRk8SXIwBsWduU4vVLLmzh1j34jNdTuhErXMbe03E5y+WvdVuOfH9hA90hHR/f82Qv0B/NhQHPANS2YAUmC/c5/lO4RZMtsf+QAAoABwAADnUEBv+T0NQUPHiJFh4hpcfafnRKZdA4fFhZg8t1DRxGg4CA3X/h2OvaPdufWN7rz8Nrg9q/aN7VgIjJywq7HIz8RL8EWQtaCR4czUR9a00pPAfat6zb4cykmZK3EtJI5RK26XgSjh4uFsgg2mYhy+urrMKONy2UkyhMs3RBAcomhA6gH00+NaT3iMhdrb6NNPf8qyS1kPfjgzAbXTACT69qn3u6c450jc1h2O2MVqPqhAmqDRNBPBeYD7tGYXea2gh8ESWDk52/8gjvOZf1+iUCGvqLkE88IGqXjZYibory2pSD5XwpzZZxBcgUbGMg8/tJzxFpFIjE2o1Va2jmFaS09gX753BFYzJdZqTgifCJXclCvIkrIKzKY1+EgVKpilwN+SfrOgPnd77dKPozTw1wkFK5JulJG/07GXy8PbYq0Zt5L6sfRwAYhws6TUlAvaA+lqC9wl0njppHwE40k6M2/fyE37DAugwsN7chnFBukH7wYLR8YAm8rWSW3xAXmdrgDjMZqfsCikXoh5K1kRzwXnlp3iCPu4YDgAjRstSxzclQoxn6CiUpR9lPv1TkQg0Oe7K10xV9jm7nEmQq9yBn4PsuqmvDgFDWhRHmbuwyzYbWMlJmDrtTsDwwOyhkvO+0O8hErx7pss8kcSF5GhYN4cqZEPkH/Uq+5wuSOuC4r0lGu4Go+iu6zZAl2bh9JvZBXGjABeuryNHmFgETsPa9oxKsFQhDujcuSSrYSu/YcPPwQFHtbchgCjrBXJtLiVOCwAf1elhEiKcrB5cQeF4ncXxS4LOQiMsDBX31pPEmqsJyMrmY/J1qwtT5es+F9N0HlasSJn1AsbE299v6M8kytARJeqw66ruPEBzYepHRlRVT1tJrdTnbfJAd0bnzBTKdZyn38ELsu/QlLKSgmOp0RzCAPx9ksrMTqjS62ykGLKkW0fb8F6AbG7rMHrkrfQ0P05nqEdn1HUrJ3AsIBiZ8wuF7KBqAlP6tU5eFHBe0exfL6XMDQzOxAXkFe+W4CE46i3wg03LcK28umcCnbYOaQ4PHH0DAMUqFCqsk95Opj1MQiRzFvPRVwT+ZfvzR995othsMxVoao2mAgOq4wZ9zoXPYw5pfNA5ZWVzZQLSNilSBZmjW5PBJCQwP/zimDFdeIiZUM7qr55WqyRdyo1PE3PGsawzBUanoP5l8Rsn5vLGM151JCvp8vDrgfiMf+Xa94hqLtJK4PZZ9XfUNLYNDbjGKw7lNoRcnRuO2DArWN9t4eDB/0GzHUEFrJxkWFty2FUX/MBiAbk5MyNcan9vLC+Mlgop03KOg8fj4T2PIwBTicCdxWeU09dhR50hURmuda/e8gXBl6K+xhAFIXJPKIjhFMrjjfkOfdiL2DzWgD0fU7+01KSVbSziiHk+9vGGcW0piMDWFA04TjhAPBKsWV+0zZC7U6ZoX5ifQ8J6x03/4Vg1BoCIjBERuoKNPeO1XZpN73ssBiWU7JCwY24cyU5CpJ7OPFb5xrfahtv7EiXQzMLKRHA6ZhHYeBoRP3AAYi1EMG1MQFe5MOWgFqupL8Krr8j4G1tOwBZnr+P2ckKpVSIT85BsxG/dNvwoFL1CcAqDHHrFRcigBYIPCHn1lXjUR/KnfgqQm+z/pKSKgxQWwxziil0i6MrNm6kLygAZg6ehCJj1WiAIcrK7S1N0p+Y8v0xOMDbbih3bz1iQukR5AMF99HwxDCrG/mImJrKY+88BlB1GHfS0yfMMAanR/HDBv2y01LFql3hgjwxu8v2xhX0vg2Jwfty7tGS7kRm5fwBG5pKiQ7YFVfs8YjBRSWFpirrDqD5to2lYYSDlMC4VNCOD9rdEzjNCntCaDllTrnm1iSvgVuwdTDicDOagAhtInIz/Gebwl4Z1b0e/fUD345ZXIC1spDDSRhzESpEOk0KgruwSqjhpBFw4jEPleqFQz5eZdc7q1yi+wIA8dwNkasXDAah1sxKiDvXL+7rA8UrpQzQ2scfrrMsIWExHuuZHQh6+TCIf1MueHzPqPyh+lnKSPFnq90Dt5BoHbVTg7iLqkCqQxGfy/O2FTVs7NrAtOn5V9PWWAbKYb2sU6bykD3qaO92x7SqW4O71xuB/GE38ZgAczKq9QgsRbw2W7UvBaJnaZYBN0f2JrCStZlBagaHhmH7QHNr9VbkklhVbYGzzcUqRXlqZ24J/sVqlbXd5+HCgT63zahHbugrIw0w915+pVULu2aWICULLguwJMWNgMJKxDfW+HhAY45fpcWDaPnYDlIF1ej/3LLz7/Jzfvho3h90jDc5yWyEKbc6/s4qTrU4SRAjDa03SwQxOvAxCiAukh9do4jiGgVNR+ut0LcPtg2OK5Ehd6R8fom5TfZ/tGEd/12rFAE8B6dwMW7fO+Hp4tNYxKcUsK46FAV+IxMTycIPsNBHEi+5Jkmfo+hOXSziMOIiB+Y5DfmQVUI7gXms1D5pJTsIoOFrKO73eaRBMaUDQOIq7nSc3krxKQ1JQXIjHcZGffqTN2MHl4ZA7u5WJpYATCM0y4vmQzPtzfzao8Ui5+D8BPaYWXKEoS6lSyEjbqzebBJfLUJOKLFSUdA1byY60f2smCstu91P7E0gz35gldb+SEp4j89GmfPEzMK/Tk+BUHFSfxZtx6LlGKAvq3ZgFvQ1qncAbLnUeU03X8g5qKf4PPGUlPQQA69yRHRusoB12go+LozxWrc/r7+4vzQZc00BurLr2PmB7QgBmObX8zLpW+nUXfVpr8fIPOJB+d4kMXc6ZXUkHH+3Qs2gPjnf2paLmvBFq76JgKo/BxEFfScXc9/0X34mfK2m7E3JB/GYf2kBSlcJMexgqm7F92BcfsbT0q2Tg5Lnt0OpbWPMsbhpz86Pd2eEHih4z7vc5t1D6qdMVKYRwsvoRnAaB46oJUUw39pKRkNSKujTwQE34ckGplwtzvT35wLObp38WAYCtQ+DKMTLLVlCCxJIpWBIWBJj6P0B5qD2gAJm4A3xlGXZP7c6I6mvT+YTPGUTZhphURH2A1Y0EHt/h1kgOITYpNDc0OKISL0ihfqdd7F5UBt1a4QIlfL9L9rh55UNJMDC2D2gRUQ0tMzGrCu5UIWewcTBXsI5FOSCbH20kJSYvwcm5lyX4D4k+FrzUmht+JIad9w6ziQMsZw9l0ye0hwsbTs/BN0O5RxkiHg4OArfANEBnVhR9/o1Wro2ROhR2ZIvl5VPGzecmwMOio4xAfnrQTVfYdNP0Q4ojOPxl+1aoiZ5xO95A7uJkQLZGAvxjo07LiAQ0JPJAnW/94s4L27Y7MtyWw8nifX6VzUr/+Y6Eguk10/OQ0zNBBIccgVMKR8eHyYMmA/nO9rc5i50aZCLLbpi/Rh3Z3A/BJPFG6X1fpvfr43NUsg+aETkAw3DnXngVutNsXQR3Q8bF3iisqj7UXQqK1m52tAuPF6c9SYPOXCJhT/Fdrz0gxJzYCj37hpam4AuZANcWgqUnLQCIM5IEGFF7UQDOQZSiH9f9Cj0qsQw5VJR6CklTeepW024Gs/az7gAaURl5eghK+8oeG5Do7ba+4/OtLMQmSKYtLakRfMnz+dhwPXmx7J3o5GluLEJjiTGZ3msnvAVsUTKVgEZw0fhNH6vGtvfm6NMH3pq9hdz2YkDNubBPLdUSRH56AsOR66kNrUdoe+W8dvLHKXuVlyxqC5TB9cz91xDwI1/XEEd7qW/WhiRrurlYnekgZ5jx43BBtnyejLTon9Ozn3FglMRjeDS0L8EfLlAYfuRc0RNBGua4w1+nQaAIGNT/tEJLegpqsWEUFyCa+Vaqhhk19Ub/FyppmyKd2ZFH7Ed0oRlewFR1kFXSDNcW2+9wfoCdUuQ7kH1is5CijE0w3NNXnyBv8x6S3qx3pqX4QaFKp1e9VYDKerjkX8qA0FWPKNdOzJuTavoR6XSVA5nNKotT1bflLnWvpK2KxggIZ6HKJzMAJpHO+e/NaQe4nRXPCUJBcbhqGMeXk44shNiYw8SbHyGuH+Uu8KiebF5KguNVGOsyt0sA6bbTXrq0nbvBronEADCB10E7P2gdwEAMy15iSTRR+/fyJsJFuOCPcb0yRoqQ85cxjyD+dQjV6ydk47DW5yjIVkyNAmV6iJnmhCAzEgcJYV7RMoPCL3p+6ISlqeZxDmgBzvX0Z98fshq0YDzQAaE1y5pSQxUtHiMd52hmbEd+O1+Ibld6fC0w+NbfO1+OCRRL07JAdwUT2VCI3bSBD1dMqBegD2qMy0TihJ9/X7hBK96jIQ6+psn39V93CsPsJDmFsPw8lGHYXqEDAKLmj4fgqbkUL9fs46n/GQlSTfvNLMB02jX064FSpXSFwJlEF7Iqr9hKbPDEifbvH2aNYGEqprl3xMeH2kqtvMxLpSfZt/djEVlNot3CwAZA6berIwZHdP7qKLnYdR6LR0cftPbZglFnGaawVgif/bJFRMkVqYy1H3IBygCyA0sW6l89z0bEXd74aoKrZvm2hI7XerPU0NiS2ndgo7SQYx8I6+3F7P7nQKGNj94JU7bhUS10VCvCEm53FP2XYj/npKi2A2fRprt/Vdp4/NVWRCcSfKbdHDlMa1yu8xDgCZGaWslUkBQYyHGVGB8hcGvFtAUiIWkjXvgjqrfmRmz6H6KdYdTBz00Cu3mJ5xU31dA5wsmYdopzG795qOQldun/71pidEufn7SCfte9Migp/KGJn73Y0YvWE7CzkRDhAG2r4h5AmRIIHtxjVCtQALsWVpwBUIKBfsGn21bvfEampjOtzyT25Uv5spa7PWDCYYDQXxMkY99C5nNsa0xkN3qQY8LBc4WTZD8gCXxXvT5Hu52d7Phewoqyht4bAK0AwrNdiUz/n7ATWZ3p/wiCMgYxLgEnbJBl1Xme9VQX5XXpBsF1VZQf8n4kUAbytt1HNgmfndlah/5AACgAIAAAJjQQG/5Ph5TeJ9QAwR+XZfo2GIoGe4rQ665VR2VuMy4DChr0khCrdzUf3q77ycdtONqV0kkmG4ICA+3lK33dndOwfUL5hh+GyXaUAOtAWxp+8AjyGGEfJ8XWpBFZIWIm2snTu+pBfDt7ZuY7FvRzQ/wDVj3bpgeRxWsdJjoPORvfL2Lw46X2xJLUGmxNj8L4+XCOYAfITkPcQNVHZGPhHjx3bYNDVqailxw+nchrQgt/1VaGZAP2mcUdR/hoOKVIaRWw5rewsi+mla+Enhm8Msc09KwH8tDkWHADDJIQEYWzOR/SaKS2kmZ3IZzO01x69np4LHfZIo4M9Ak6vcB7JthRShR6BskrxBh19ezHUF9whPzL04GqDXb4ktHocY220Mq0HkWp+Bc8Ce3IZuku8qupl9jogEVwdeIcuuCTMv0qmndIMYmowd6TrN/n3We6f/QZkyDVWeOjLYa8zD4+G39V7cBKQ3P61z1nEpglrc6taqZRe2I1+isUT8Ih3IcJ/t+xz/TCFzZ6bFj0pm9qT6s5vKbpA9b2szoaIz+iHl3jJFBQGelPmp+129E5vSUl7fdmvwMAYjG9y1cJxMVhNsGmU1TtOSnCMaMfLKpHJkYjtf6+n3GkCecHJfrHkTh7NuXh/YQHO2ioF1IqWxfFW7sORYjHT3Kw62VJ/i0pqoqQ8VDmnn6zcfs934dTaoYDeOqJUba78iz5lU9u4yQ8/u6e1UVibZq9ru1JP+a6gVPyDqh7t+cCoiCzr7H2VFsoNC4f/V5NG/hcqHMkPL0m5KJnwR5AARHgwHzzC/BY39znbnDZQ19LR/P816BT1hiNncPybhR7UcTDCWV+poJDjznN6jaGMk6TM/wjz/O0y/jVPAhqmeWKUpGuSQ6sWeoKyO4kxXm6MqExSpFbYBc9DNUbQTNFAC4nfztNPuMHgx84CIwhEQjJb/EsQ96OJ3nMQIHmeQK1u2YUSuTtTpMAW9ZdtamNOQ+B6m94bLLNjNYQBOyOKlXm1OR3XF5dCE0RoKEKlpMMX5UucnzLxsXH1EVBAzOiAgOKsTjGINVpeag5os4+/xivDOrFOXIZi3YREg4IwvhUypLPjgoFQcfn3zuILcwpNDNZMXB904rfTZ3GWHekyfUQfzfZVePoTpA2nuasEjjPSX9Gj/xlbQPcAF9OlpWHC9/+FKNDi/2pqqXo8JHj5mDZ2Bwjvx9FIboWVlaryXvjfEB4f9ZWGoowQe2iS17/iAe6FM7LVXp+a8bMbU8+zwh05rG5Ir1JrW/fNMjJq3sXgp+0kWFE7b2YTw6ftR1eXYdrZgbPWMq57fSj5T8PqAdZ6TmLtB4n25nCoEzhw0PetNRqqxBfhGyKjulZnbfSZoNEU9jtYRgFF64MZiss26URM/hgYJ5wHYkIPtDFGGF0A0OSqp6gjuQ9Qc+7wp1SFW0pzdF+bap9oxATiYB9LSj2h894By/i6gIDVZVrUHKgmwBN27FBA8JQdPAiUllQau/yQLms4roIX4uidDxMG0FyxeU4QPgrC+xk6qpZp6IfZJxTOr9LCrNBumKqZMoIdwabC6TDn+H99J6jdLsOaPJ+btY1yqquMVP1eLvdm9BJKRtp6rwC5EbgB7BaRgNSzPfuTeH5gpLMC0bw3oyr12HsQNCZup4fDjWR37Uts2n6CtnSTt6wjMl7cb0gIsEWmTO8Zkp6Fy7dAEofoni5cTDh7o9eoJaFVaZ/7kHvZDGxN4bmcZnkO03TPEyiMXYpK5Fxn1lvaCe7+Jj7FIa3bMtrsFh0cEq51vLyXF0GsyrKRlDoMlvZsMEdFhhAsqe2WmpbtQX+K1q1+tBIhwLeZrby/z8p9h4u6ovTGTtUXfW1jPs7aAZUNbqQg+QOaJfy4692D0vioTtJEmUw6CNbgN1bdgC+b3z2lT97LpI2ZMRLl6H4oQgJvMXVOilcjoHN+Y3k8OXWWkkc3Kg8R/OOPGlsGHuEdDKmzSRd7gep788JwhFJPB19PI3rxBs4uCre/V7CxO/83eQtaO498sXaAutzWStdFqoiGUNe5do2aBFAXnb4lNDEyY9+14ACK6wLFIkNPbbjRfG3MojkAqHf/b1ugasG5YiYqf+u5Ywaw6b7BHsSKNXXT38wkOSfTdhaRVMurMkmJLgQHdqFrm4XG1oUiSg0Rt0QPZn3Dj7wtW2Oduwx2b9JJ+uqAHIB+exAvU7nZbknty8YNb+7I2FTaT5pDZ5eth6pPRfyzjzdcimvEREpNCm27DvXd0If5zPSe0xCN7hmDdsY0BfmgFydLhS8D6wtP2hJ2n5Md94MomO8FCTQ+8Pa4P0jlZGnxt3rEQs51t9jEErWC9b10M4R+LgIyUSwMw5bwf4cA9kwmEuWWXfLIRi9I7FNc8mbrJCTI1qy9le/Ly0DK7/1ok/mLysrI0Ox/ICzgJ5JA1Q14E2ZglhQgWBAn3n49yY9cwxwJUiGrcCYIix6tlfXcOvU/sVpeumL4Aq1JgHV66D0usMfH5FmUO0lsPOyVcJ2GLJBOzRAniVm4NkCocX0rOua1hXo6UENZB3tkiO/5ctw/kMZRVeZb0EVnlRIXUC2gINhMKhU4Rgx0wZ3C+lwfw3DSSE4lScKdQxscUqS1/0BHZPAZ9ApH4atMDCk+BT4czm3tJqQ5gOIzEiMEOwqIwESK4FQVwXqqOHPIn9Vlb+OdJcahK7K46F4AmSoCfbbPbx08Z+KWkTIdQrv07jqHRmDzYrFbX/QlTrqzPjBAb+4asvx18Ve3zTa7JogM5a3+BMIMz3SDrKxpLsaejpwuMN27uru7FepdnweYtNez5XG7A4rBGqrRbEDPMJ/aatiltusEn1BKj2h1Tc34lv0dIoftHg/jb3ugm8lql9iqZDfREEfZl1ZAKtqdRD7Zk06eeqmns8D77t62pfpRk7VK+Su9k8sSmzPKbt+xfgj2CpqduC0Dop7RFIfYzROLbJAeOJ4adT079FMRHjyEKZq3CJ9psTazXc+JoT/vxcLHXobNQlVrrc6PEyokEG5Rd47wN2RRIZHyhFvNmCL2SEce8eJuaxGWoXk7zRdpRQxIK6rn+xzz+X47C8yroyh7A2KZBe3HWM4WlkYBc9CUvLk2YKsmsZswp9aL6Z6Q4x5wjxp/ZrzXcyegwrF8Sa24WLTqLtPgZQlIw/YNiTCb6YItoSxZ5sRnasR3RBBrx07rRMh/4Dy6AC+b47/mSp2ZA/gpYtGA9Vi00FRTvs41mz6Pt3ZQ/5AACgAJAAAAIAQG/5OAgICAgICAgICAgICAgICAgID/kAAKAAoAAAW2BAb/k9DxoMbBDxE3fYtJzOGh1r+AgM1b3Ri7riIPHocfFufsB+iMLNClDY+Lits9X8CXEhtlFWEsnE4SSgsZCfW6l8unVFH/eHi8zQ8CvtaaW7WjP0AxDfIgEDtejbMmaSSNFjcp43UoQ4VfFhRt4WU4kXFe9f9oW2Yj6PAsuy0F8G+kpzUB8ItjdseQV/r/WFRVBlVD30qLcaM2mCvotGRq8xUGMtT6n20GHhHx9wzdErWs6iejUa1A+6ktjFAhhV8GjNZC5SeEt6at5sxX+mtfECE0HYFyeUSKCLA0ls6wr+ns+oBzRAWmy2zf+J+7nLUrh4JSwGl27TLIjkgGt0CoZ2u231qTJWCJD9kVYkw3vtERCkYHYNbpit/C0xtW2XW2s2N+seSHbb0qgIDicbTjeaQLiXKIvfYYxmbTj4I51Bygqh8uA0Eq9BO70BC4JSa93UgUo2okxKEIm4TKdG5vJsNEG3C8rtVxINl4f2g7VTAerlyAiUq9/AnnTUhaaeppJ+joXQUto+880DSVrhYJmFQFac+O5E5V5LgSoobU0FHiLf1/AkLuI+cnAl4cDhscdQC+BcBfaJMVjWDW01A2rS4QlMHMqgsteFsUWhAuYutUDE+5z6RGgNa9wxvME8U9yVzHAs/2atgGKnEx3t1wIqz9I0JVdx0/rwh77pEiMos010H1BKZJtzyL5x8I8nEsy9RkZEdgEn08PN2djXX8kc99U7VlZ+Lcriz60BumADmdpebTC8AxBbgxz3S3rYlqLWxsC/WSS6Yped6c4fM55yySESMxgMyrEUxxAa4VAOi5OVrZmBmm+iMQdGl0rA+b0D4/6CNM5Jk54Fq5K8H0ZOJLMiTqORrkKBZQH8M5KDP4jTBl4BvdoipF1qmOMOV0avK0ZZ1nZmtTJvkrPHCG1pR5ML4qwN6kgtcHwhzYnv9npQV2CLnSecZ1NbFwHX4gaNYv+p0/+YSCZO33oa4fLNtsIBIrHfcXqv5w9Ekvgxi5PaicGVrGlssfsmpDkpwTR8S57lDxWLcsfNSsKznFeZaF41enEdG6vZvBQI601L67YLlBOZeYucqdan1Ol3ImgCADOAcvrcSzUz64b0/RIGeexkCh5489LgBJQKAPxdeWsnDx996dZZugzbLTvANsN9yyqFxpmCMIa7LIMJmxqSFTVgDbtdKgkv4vnN47hSHQnEhdQmtQgOeegLlr6CysrB4QuFJWkQZElyJl4x9qmpEUUhOcpzcEGZe2h0afMxfaM7lEJ+19JVcDFdanu/6ynG9l3vsXRgXb4EOdg1FAOnVUMIN+Fch1lUdyQBqP9qTFgJGE9yqlwHA02x5X23doRyD2k05y0xNQhKYp+SJ1yXA5Zp+R1IMA+5/IanTaNLJb9XgDfXrJO5TT1rfGpgBTe5UlC11bspo4AFOv18E7G5DBN74Ej0vXNCSjga+uZroZEOB6pDqWPl8Zwa7rQI1JaoxfAoc2DBgcNV4w7lrtWA7rKonvOXGsAMsrq99RcECL9Z7WliDkKxC9COTJyVn+WDbV0Je/sewa4QlbOnRwi4jC/IZkT5ZICT8OjcG7KQGWThe9GC84JY42GTBaSVPwDmiTneSAycOGHrx4IcCZ06y0pHBTExYzu1BqNixebOh7GB0jZbxJeJBLLPd5ZunGcn1ENC4tsehl8ohA4CZO7oXHMOVC2HpJlX9KkxbA4+Z5I+I1oRg9ZRH1Q53grrPOtk783IIviUubGazwx4qnr9ViDRVurJFodrvwU0wrdtNuyvqXKxstYLY9xnfDHotdVIm1RU2Y0jVAxyAVuJMs+Oc61sy0AIjlsixzvZMN1mwpzHd5PdwfLrit4SLjpR8Dvj8ZYLyFGXidpeUmmyVahc7IUXmPqnwIWtueqa/SyJ+iqT3NNYXcpMN1+DXQHiQA2Caoz1UmwZpv/5AACgALAAANrQQG/5OAgIDg+6ue656rhK6+Dcb1T+G8we1/2y+1PrxGNOMXZtsYY6GaZ/Q7i7fcnxLIKlsyepeEqAUzVVwfHWLdzRKD5KvQtD3rDMZJP6Uh7BTmXJbwkrQ3CfW2y4w/s65VBaniTRbN15sG9wdOPIDQJ1p+PMNKHVsXBPv/M7NLTTxWR8cUzqffzl4lYNgxx1iHt7JGkMgNAgiDJrh1OeCNi6KkXmLSn+WzXgwwwenfaif+dJJ3NGXIJNdhj80F2Dkl9iD3gkJmR8nhsoswM223/FBSL162MnVfEUyROWc/TDdjcbMLlM5uCMT2xY4saknQCs3i2fEJ7vgBhU5n4IRIJknkAdWjLpKtNP2nsG1D85BIV8GRajTGdV2HO9BJP9kYr1r06orYPtcrg92gTLHCgNITm2KeVzEfmKCbnMC50UcyEUtCg3TNFhSDTkw3ITRoqAO9M2vdbqXZ1y02osb55PvvPo8AbApRZlYr7+U5PBliRp1E2c3c3+bFfuo6de80m1Qt0/E8x3N926qY4E2nq6xK1vtuR28m9/OUH0IFyzgHa/A/pDTgt+oyRKzVbf6yczfHbHAX4zpEeYfKWS2DymRNWNasCjfIJ+kG8EEOfYFGmRKc9LUCs2B3CyQFJxlxIZ4YJuMbUakDwYh2g/XZ6yjUIGFq2zLcR3vFey+b5sf32lClk45uhHMZM7Ha5QQsyh7WhLqd6AnChL+3kHwPsYN3R8+WCk1LWWYfj2JY6xeHLR2Gax0LdzbCm7cQ9US4auq3zvvXkNx/r2E6aZ/mRagORr7fQDyk7XeMPtyEvXz4qmM7j6Z3R7Qfaa/Gz1iGsarq3A9BgTBw+39MfY6IkBXxbe6BVhk8P1arGWdX5TjIr8BVf2ynS1EthIapKLRDC/jCxDMXL6uIWSN1bhn6BpNX/s46gUY0bTNNUEOLu1via4KSKTgAYkxtIK+jJ4DyJwxHC3wpsfCbTsEdm89wMUIOrUnIDVZ6FbiMHouMFBXlXrPHKuO8Sz3wOYCA40uUuaC1HGVtluanzecU5d86kLZjVXMM5gIh5Xfq3Pw7yFcV5VNVnmmcjH+vfG8X3F1zbO2vPygvAD0pfqIeXT2GwXqmObIYJK1t+ZRPIphl9QN/el22isTnHJNM4sCf0KGs2L7OIuoXg4XIB+esi+lobvSeBebg3aj/TVm4RbESHAnn9IeKiFKKKszDo/LgPXUstj3QXf8BlxC0XOHiJBoIZsxMQ0xRuO64tdu++STSTUdH5ypOeryVfkJ/bW/R/ho1bmCn6xV6leP9N9Z6cXNYCCOt0seCgyyiTtA4qFxmaKC5NxVmAvQtfStk7kqtbwzlK8ISRSuwgoGLL+4lX9rRMgPpbAhNOE03QUn29EBffk2LD2wHE5NbnV8MfnBYC2Me9dQpHhqIjAGUlSsV3nk17MtWFbPlUuu2y6OKKQJfdwrQiFnKa4vos2vzgjEjs+qpw6y0nmkxdJggPiasX/YIOGoeBGbaJ6JdjbPdUFzr/KWHxTy43klmdH0cS2OOxanUbYs5HGFpG1hUaYdTz92ZVQ5sFf5RR/G5vPR+2oh8xB1b0UuF4mm9t64yjitWkfn5qAS9R6ReTziEnpcyRtiJCcoat7W7VDx1RuN9og9tbpgilebXJLEdn4tEfFx+pviIz8qCOO9flsHSfzBEpS3CK9rcLbk7ZspIo/OZmjtkNO4F9HtGvoCwXhWNfQc2e6TbnkJzp76atrP45z4yfyckvLmi3A/8Afk8PVBBiEVljFQdp7qWYYWJbLIb87X5JcEYAvKzktmcguTSdOuNkHiDo8kt1dCOZ5qwfj7vWGWuvNrNoBeiwgnjJhOiwl4qUAj2kB+/YRmcYRXt2eQzzJ30UlKP7CH2HV/3CFupV8sLUMbzyJoiIx2ChyU7XlzkPseJ0Y4WmLxh/SlaouPrz+iAgNXOaXM9Xrq0KsFXVYBhLQ8CKKwoGQfOUZvFqTfdf68KJsEeTaORNDbw8Ic/pPjB17jSwu7C09Id9XPlyH6NlAfEcc1O4ZBPw+QZgsCsN7p3/RdRqPA3E1mQ12SS0NxMCqSdnJ8Kz7z8a2s879mbOiUgF3ZWiDjHra2tqcBwDsUIsBSahG3u+rsIuKmJXuLIIE3jGU5aJ6KLujxtwLbFTi42AQdNDrxkJ0rlvWTHSsExkfynBAWAhDvHf0k17C6rKjWyIEOUs8OEjm2TPMIPObJy/yGlM8BpY7NIYLV5pKJiiVSC/k4BzPVW7EDFNca0V97hMgHYlpB/rPtVYRj8DnMQ1FVi7kFmreUNqbFJAfI9cQwfA8cxw+ohD2a/5hiTgboAh1KuCyDJmNL1kZ8WbTw1N+TDmI4M/XI+Nnz3j9viNLtyKWnEqBS58LOhb+RRdY+0/fFf9KsIe822QdXI5hpVGu+SsMTaRb/352UYTVxxQDO+jzYM+Yb9yqBs2/c78dU5d9LpewTsQOQ7ByxaTzVj8bb6k9zelMbflovzGgeV20lCz0QCRhogi2RHYrC5wAQjjg8BLg4mRKt8ewFXGYsyWym7prGxlBURPHYo5bUD6/kqALyfxf09msxLR0tV76AKjFiaLup/l5wrrPHFlFZAAqNwmPajyAD84R2/iZzanrSovAciLgWhBUFVxzIbhLXEat33QhyTF1a+L2qgeVPwS9uIZwymXef24FdM/BdgHdGo7jTxMQkma4rACcnI6Ya3recnfHMvlMZf7oIf3UVqnev8oAPJ0NyL1uLfK5LyzFidXfDXm/ksJ2k3a0aLFpzVxYU/9Txj7cRwU3omnDmUdav4xAP8EW4iHy2C5e7BB4CMwzytbM0OGcjA/UZm/S9+5OIH8HyAZ2hzPKNiApaLoaW9NU21u1OQxPgcEElKezojzmG1fE6ZF2mHG4tunCoLNo9z4y3PG7qWcbbzDntS6Zg1zfGokP1Iwdpa2BfWecL/OGY/RbppzNDawyo5yHNhTx24bUY+VUUc059rrfErp7VDBvBqP7NRl8gx0XUL2TOakfVEYNrd8vx509mjai2iPmINiio0Lj2/JOrvcZpGZSBZxKryDkFar2bLsFWvv4A9Nnuw4IE1vWv1neLvUfNe1UL2LOqCuupCpFylDmBwFR3jRh0TpLDFkNPIN6tJdJDZzXpWB+umqX5VpGI0JJSOBSrpc7KFmPyjoyfycOxtsP7jigXEwk75yb4/GGV59eBl5JBcI7GYJ0SgwWh1GS/OIFcsoxrb3NZF5WdbBX3nYMQ04Rn08aMqnzupkyxK/xdhcO1wyhT/ZVrSSqssQ7gqNw4imSRqkgvT5MyhKPZHTmzKEPpR70LtxU+y675GGOgqDWDEkHeqhooJYD/2PnHlXbybAkFVroVw17lqPVnKH6P/encfWD8VlJ1f85hsqRuUJo48Eq05XYkcTSlGQkb1fGFavzkGaNT6cJNFgZo4FTW66CTkAuPoDy4809AeabzR+UyRksYQkzQ8/YrxnJaxhP90esNpfLYGjrAqA/Wujowz+toOfZZgkIqh+nvJSnSHojdTdKsV5CAl0Zsgce4X34hB4c/jpQdjXp/Tdg1caMiDOUdH21vY8Z7KWQDHMOgLvkS6xYKjqQE8VB7Tm8YUYohU0GhXFKh28UX+D02mrTajH7HKKNGz+sZE3vZIZ+Z58S4n7xVSR0HdlSgtl7gdVvFtjG5z27dE8e9LAdnLbs/BbqKi/A36dicPSebwadxzk4b6YTbZNwHqz1XzUenBDTl8xBypbd31QVFIClbiAGy6n3hzbNDreYRJGC3zZXGfyA8sALf1nm4QZf9C1soTV3VE9XJWRYphUfQ6beHCMSI4XI4UjWK4wPRMBoWtBclABPoZfqdY9vuwXHi+jkWgzgp5oHrEcZG0Hr2Cw+kEgcnODE4J0X/22HV+uz8SP0UWCj1jlatYRvU63Hx6UKhtmSO5si9bfrGEifjjHQ7ZWRNdBOkQYnh6KgFOGMRgZRLKo8S8YK999fY23k5WvrsVZ7lOAoj4iScV1vFFl68pMFX/In4I5ll9eJnT8auM9PHARDuiESjnnZWVMW6fO/NdLsI59kT8weFrfL72sMuZY4gQjysUHVbh+UM+JaaUqNL7F7fZRpt74HwLEeyQnVlvl4ttbNNGW8KKQSUdyxeBkb2tnJ06forZ+NT+H4tqWNQg3Vt4ALzRkJj+Uo8kM1Hitqn/K8o1a5iEwvCIzM+NW/hHpwuenqZDyAheW8D/XmlbSJnby0LqK0T3VZoAwbuHAg9j41ZNZL05T9zJeS77Yj+5xV8LGFGelR8kQNT08Nj84h9Wfe/2Y8ulIgyuOzTtXWT3Cjgh3AOV+mxDwjS7mVUV2MDzQxWZFmrORTEwwhWCoMJqW3VpMqc/3iUz2DJZ79nbiy5H3yXsiXEarttQDeeOYHhrAgaf4BSbfyZrJOqHJ3IULHZSqqkOJv945kHB2/4Ok83sTLadPV+DrUsdawRogZqM4UWUF9IPCRBk4oiwIw9I+IN3fweTHOoWvSqodDr52Fm79vtRDlVcqTxwHit9O2B6MS7xK0cXUM3Fzd215xYrhmpEQgB9atZSGan3ox6M2dLI5u0U08Ux3OBRYOiloXd4gH97OlOY1MdFpRnWODg245BwErhnzbXHBm00xLC+Rb9G49CjwdG8YWKA/5AACgAMAAAMxAQG/5OAgIDQ/DXrtXg+3f7b/um/aeh+HU7pV2qAh0GGl+ijaTOlL6u0s4afPL4nBuGj4P5BZGwZf206/wFCu9Qo7pezZo28KdsF1g+lsEsiMcbpTQIFHSKoVp2S2Wa3XeAmKK8NSxVWr1q5Y/k367CpykIg5Szj4sQRdR0vR99eeUy52BUVsvqq5z85F1EbuPb+1DZ9bplMzk2YXrerFS9OoR6sYuofTl7rh3PmoJitRjNNvTB6ikmxzWOpjKHOPuW6pyd5all8dDBvn7Op5Fm7090gz2n40ySMNJ2u3szRNfAZxm2NAsaw6Wi0yYeE3efVUtxwFV3TE1YaU+ob9p24QZYrVLII1X6klRel2FokFZ5c4D/lkzWbNBjH+ExsJw2bCpdLi98E8dWy4ZKqKlYlTyv9GwYd7mRFPWe10/3vqqor9KxHcC2j77XbL/NE0O4xJ8LBJ0Dd9b9B6JzElA5gDY1ZERdL2mxe2YBXHTZEr55oEX6K5UIqANRcCnieOwC1ixz8NnaQrGNkZwyiJ7VgB3ubcQUcZALHnI0NGoFnl9ynQzJv9MDGGj8QhpqAcVKHPS5w8Z1DOv94mBZtk1XZWrp5aR2Z/l6rAsnJG4SRp8vaJuNVKu6oLN/VepZUR8zBw3++R53I63YXt/q60Dd3NGUE2a/t4No3Ovnw/Ny/TZGbHFd1VUeyQKQ0BNE9rg9RnP8oj3IYrXDh1Xf5SarbJ32onzHIGtRUdQYKuzeKvK55jILdKehblvqLmzt80xkvoMhczIAxx7eIxnahUWUYeVJ1AIEzdPUsltQtTJ74ezJ1eNLeEBNX8swN5EZjxiBO3WtAGGHqeiL28M43taXLeIwx3RfOuFJJcHUosb33NWlRkTPdetPg047gHExdnNrny3AdmoCAtTOaVqBluP8pHL3y5qU1h5xcmXC9rrGlsIw7HCdnp4w3eiMUwEqUd8E+hHkiYPGel4rDSKlX+EWVA13vDWYBI5KaZVIgiis2yKRMRq0+BNJ2zm08iwJtfOjIyF6ftRgFwiVKi1aq5v4wItEfiEqGQoVUcZRhZ10ksM9pP3cUGKfatSSfA2Z+MCuhTOFFLiku428lveddJgB452RVbcahlbne3DTaSZe3N6cvLphtJrkO823g0r0x9byzFTrvzMtDb+qdkR5QBjchJMif9PLXvCmCVojHrn5lgRG3aJGUhIjmD26dob53YBq58MZpphhE2pViI5HLi8R04MBdFK38Gf9dT5/7C3wDKHSLxaWrFEvKejNOH8Fzjiz4+/5EwhnRg0qXZtsleeYPp0drg3AMAn2WkyyxKZ0WbfNsfjEOWeKDPz4mtzAvMD/vImgaRzD/AMJeBty8FRwCn+Dmdclx03fe0lTlUHK528stw+gQD2+xuci0j/RXJg07TKWTZ+J6MAbGpC1/Okf1PIqX1rCQ1hcOESBH6kwgqJBC8FijyEFSR6A1dObBeqtazWkspsL8XPzcg/0jqS/9RV6gXI+tx5bmNeIo/VO+cBEOZilmfYcmQFR94jWKsKbIzG+hdN57s23LhxOT+8SIC7ORGLFfIL8b3Di8J1iVVK7inmMx157ZNPd9O7r4hv9ygIC1jKu2rVv1WVaT4JER7T7QDcS/2iBrtaYcw7HVOKbODFWIEvtmGYrWqdudxwJNj69gAsS+RREBtVl3NPg569KyE2YjMkO5xV5Ma4CRw6bTJmcgRv8306Pp/FDFVi72/1OU4uCrDfHD2RUMOrl8BdU1Q4b5x2Fjjek2OfU+JVUGhDx+0FCU81h5xg4dzzKK7b5kGONrji+2t6E1OLeoIqw/JDSFGASukjPAsTvbEjVOX7yPMD6D5pM34OabUfQ+f/PH2n6ZzoFzhcI9xNO7bvtMo2/0VLWKzxjtsudMJRiyRRLA2scxWDRhepFYFMZZaoi6NSQS8D4vq4cqBJOCEB/Oku5hV7SmB9VkPFyNBFw0Ozmt96dqnmRgrt6r9KcUycE8g3Su60w/0bcfw2hfrLxavCKxpMDerxDj72TccWtPtOSUWNOM6gj03e3qocqjLwqiLZi55TIkQLSbcCjg8Jv/C/R2WIVnxMyTKl7YM0eaA+DIdkVifct9rYTn+6GFsoKyX0O1Juv231RKBg2ZEu4EuRa0Bt5eGJvwpM1axXGFVh6UalI9+te31KgNMV/LG4/IPVXyXna/+wT3dztNfYZZcChSuX5pjVW/gknfrjpeGQjHYjuUU/t2ndeZSMsa9h11jaNwGHYytt369WUu0ljy+nE6pkpa3oWPHoj5rtvMeiL9R9PO0yFAJpTkvAF4MZ7c0NuwE+jZX8aAPof0IqcIB1nWLgTsU0o/12PAg1oZ8/16yEbOHeql5hDS7usyapPvbXceiJf5REn9duZ3uxx2dCToOFmqCRWKLgqE0YWK0BtIwHdMbvQ7QE/Ajy0bLep/dhx5G14goUKmMHJbqoJTbaz4zNCm3XDkn6EXzhM30D97sQq4RAALgkxStJ57PdoCTMEqLpeq2lhwIBQfabu3mQKh7q0XPt+Z3DfgG7Pw5cja1hBbZF6qf2gQIiN1t3L6Gk36E2fYSyGB9IUsDYKM2FQUCfKYgqyep5fH/bzoPBDgL+khFiIsY7dgE7Fmh0hyTICikcfm+J4hCtawgH6yzvbWZ+X6lpgWabjkI0qGzxfi1DZtcuW+o7aVyz0HXkxS8fDUxkEx3XP1/vTAbl8XcCSlBQmFufsqNJkUHF/i2uSVhvMPY/SJ1gt+mNE76UN79aCLVILx6CdaoQCRWd7+jypwSgnl3wU3g5WjGevKDa6qkx4Z4bAx+ycypBy8bGQ9ai1pzLO3EI1WzZ7hCRFnZbUIC3a2fWSiRbEgTTgwc7e12esBvsdw+IiwPfMq57l+F2jvvlu15/50ZCHQtj6iTbxHlDqsZGkWEhl2sMGAx04U6EUhYVzbQKW4aXY1fwMTC9/ilk6AOFSAr7mfDc33F5zSTGoo7JXDLKTqPQKptif32qc/YfYKwlkslLGilpYZ7HrLJHDVQRlWpekAz2gm51ge5sNk+lDGLuoayVxpow6jttzZBJFu2f8g36AA/3wgLoIaw+CRxJ0esCWArssb8hoAsNIH5peUWoDzReUfHFfTu9gGs30ZYjjCxzwbXHXO+d8t4mE+cHlAb1GTxJp965GUcL9Ywp5JY+5iW+rOuMdHpqt6zNJQSYdQTfreICsdzubV+h+FZF4mrAkOr7aNKQWn2mGtU+TZ3azV/YaF16mEINv/K+ZqiD7SuppWuGRIwCpP69XLRY8QCNtOPh7K+TragUjj+HGO0iUK/Qx26O5MCuJjr41aR19R0N4l0jGBxO/y6O3xZJXOs9V1NNTvCrV3yQH5eq7A7Ym6BwmRqasEljbjvwfjZaAbD68pl/UUunRU8IXVW/3JzQvdvEV7jvHbtcj0P/3cNWKK/KqOF3y0JcMEa02m8xos/s6vqPNkpAeWQNCEz6AKctYgQWUEhWqF1RYONYg1zdVbLjulSWGG0PYhZHIAFZusULK+Y2oaz4gCVDLq/xGb3Niv9ZI2AxiD4SHk+ERhGsYmc+R/G266tOOoyMG9sh4Dg7niy3bfpIGThVUYzj2TC8j3u8pej7XBHqOMe6wmRWIpca8koSDqvVNVxnu3z6kNg48K3bLbXQVvhQvG2hHHL/rHQP6xt2XwVHT2+WqJ1owerm5anG5hj0qDl7FBBs9zLurtpKu4t9mE8HLMfowS/z/mkGmISpHU8B9/66v7PVQbfrcV3cUiU/i66QcFzC/iF0EWCrk3WJ/Ltt0R669eFkqJc/9uj9KUnQcEjr+ibiX8ysniX24yYnWiXjupqiZhS3eiJBDUpQ/v4y7Dn/metgXV853DDFAGkWEc0SpCMQUbj2luS842leBTWrsegrx7dLQ6vdMANt+TKi2oEgYPegsBkC3kwh4e9vTSyCHPL/eibgLPrblIo6QBX9ret4N9aVisg+LFKheKMCIofroStnY82G7pIP9bHxB4L2NoIu8KmEVx0cRd53gc7MkAW6lUa/VXdtFbEvSLRzF5w0JOW6uf4tCuc+EkHmHHEAJkenI9WEjpC+iyv7kdHQ313Gky7e15zmVsjtU69kblIY2BCXikPfvaliFjgqsqq5dEz8bNMSLW2PbUajPa3VoGwt9M2Yj7Fgvy1VQ//vbaRSHmoZFR960LrCq/bO+/LvbMWTFEe2F9F6hfpH340/krz1wphQspErGDaSuSW33qi6YAhyD1FnR+9znwtwaFOmfjtmEUKAXpKkZr8rpVIl5+4QWrSEi1ZUkMzrg+vK43ByOAJqWDfUvtprpC55M2XYLx10Z72CkrG8WA2ELLhf+QAAoADQAACOoEBv+T4eP9T8aAcu4qChdJQk5MRP1wz8m+OKx7XdvpRu8cDTlaSie/kPkNSKoVyrvfHHI/91fY1NKAgPute6R3bLtJIPDMFbL2jL/Lj4ZLljuiA9XfzsuBaFU5f9AHu2zHod3XpBN4k4cmPEgXLiY9HzAnZTcNWWsT8Je02ItVKQmhJRDzr8vPEGccDjd4rO+l5ZbcvfMOrezUd40StUlcVOT763w6KBbL27kVxdK5E4WbTYqpkLohZwX+t3LSUbqwb7Ek0umPTJ4BDkb7I+xmGLNEhIjiyOFfvd7ClfCCb7w0A1Rm/12JTVhWsAD6PYcUsswu7tg5eY9R6CO25Je6kMHpFjNFx2+ipnKKgea3FSIv23NJBWQfc3cRD3fujP9J/AQYqcKrm/t1KYOolrYOdASEPKZN8DKPpBsUbZpPtz3C1gvSqYnqwW3fFtrNcZde5MKNzy20gAGYVQDyY3kATG6x3mKLMn+SckTbnnoDSw1GBpLsF0vBp+zF0ghffGtXsHkRzk2hRb2CYagD3MKFQ8zspd1oJ3yfOD38aRaGe40bMr65+6gBnCZQNuduuhbdif0ZjnFEJUH14YKIZRlHnhabNbrIQF4naIoiLgxo5pY7GE9g8I3ihb9JXhMpPaCQdFAMnyGMStPFQdnev+Xi+8moshHQkeZgGi73P8GeLllVyV4d9B5A7xsRyFbtu57SM4dFYpG1vyyAgOJ4jmjeXlK4tx65aIPm090v/1V90Xr7cJeghxm9QSMdjLVD4UMvz4JiYFK1+Uf2f+DMV/99HvKoyKlxMamvXabYJa9cAFrDUCMW6YgP9s8URNeu7zUmBj8Fz5pYroU1Ysf6EvO0FMw4gp4AnjK4szc7GMng03/tboOgmNMaZsCFtPnlpI3U0djVNPYuQ/vGvfnVhBqvgNG4TNYihIEs+W7fdE+07wstQ89649BVv81YY2pmyYkn5tUcbi25WJW9HeKu4r0Gl8KYrAwXL0XgHPKIXXgJqChW/qztlAiMRzYAMuB2locsC6YBuVfAs1QojJQq9AKdSH5R773eZ8itiFaTZAjdrmNg6OcBilZa2fBIcyjcwHJgelv052fO56vE4RyK7sAFw7XDkdhJWoZlmErYHofa/ZLC6wsdFdtFTtcQQE8UDgEVoclEojuPNRl23waJ+J+QE/vaFd2I95O+F6WFnZmduquq05L4tZExhGtvJU0Hb2A1n58v0o/f4fKOK2FUfW3HgIDWXUiXbReU1pLQ1WBwNX+yS7ykZQzRrTzfPy7SJVoyi6YNm81LCFTSn0EZzF/SJL/gUXEGfo1p6Df9J42rGwdrtWpcZvSE0xCi4HYoNgtw2MavbYILFLDhWJr/JjnTpJaAv60gid/W9qH/NHAOdusYljPqSTshsgco/10VfsnV43HXSwRnx9GqonXXkYqIkBeWJwuLxlEhA7kqrV9em9QzCqTNjmDa3Hqp+eOiPK6fi8OYkU20l2L28IL76TUWLXnP++jukRXVkISvuj+KeKoMWlzrnd23EQEeAR2cAKlpZfnGzy+8zVw+Dbo/dLHNfoEdm/0Ttr/DpcPqs04RPpkAr+hjrR7PLUmkqN307M6FACISspbxbr9z6uUrfD21cn9rsrWt7coYyPkoUaIJNYHDKzxbZkSja53h5d/5y1RI5Gk3P0yHtBz45GVVovXDDb16VORgc1E4x4DeXzkFHg/mi+iJQXEuM9XGeHy2kzrDIcXzI16+pRp2jPahZXu6QKlAxZYpDGfrgBWHyWyzue9XnaLJjpW3jA8Xz22cbD7GINfOO4nKehHDOhZ9A1elGvorJHOXH8r+hrmA9+8JuifSyFBnqw05Mw3MGZ7pwqlEEX94FUvIyKyZO/ENISxDKwPIL5IckYE+8ug+TYKakGbgN0m0gAbAkxrnpuA43vC16JXSUg6bgzRQ6s4+zvLnfSbDZE7MjR2kSkDPb3oltXrk276m5/qKflAqG4CxeGMgYL7j9quNXhrA3aVIncab24fuRYObHZGvlyyfcL1oiOsv2CDtd0gAFFIuAUJB7GbjuQnQsRLwwXe2tZSZH+elXlwg4DI/44Z58oXE/Oh+WUgOaI6DQ1siOEBDhQTuPRqZEx9PSZrE7c6luPKroIwLDcOIpDB8VimMuNjRnkRLziOJCDw+PBQuRH+xNte/n+6djl8rOpTUD4u4+p1BjtM36OpiSsg+geNa8Hv7kCYD8Y/YfW72H6jrMm0VKbeNijPz2K+WdSGLCpbYsBlKtUe+WnqtujPuYjf7mWmXUWaAH/EvPSADlBbobNRdhLHQSMKA5ZpPVAiEJQovoOzCWq8ZOVQJlw89Ds542pdjMrlBsEcSVIZbwYHYIRHyT7dGv59pN6RHP43hlwbke5cKbDyapye+TB5agL/sVWCe8m8dpzC8Ai6ydOYvpopo6K69Q3h2MDwogMYsRMHc8TjsLGIJyiCXTShcGJftpL0H9UWRGCnMj+n49zIbwPH/KUH59F7ebUZxXvuJdvueH3EC119+bC6QuM5Y4hoxfoL46b9iE5AsFuM5N4a3+xRqLVWsnc6iXESz6u+BM8OzzmQFfmNJQR3ir1+yId/NhV3PnBFDl8NM6hDBRfJb9NHiOyC39jTUhmv09MmckH7hdWxD94oiNJ3MzqCCvxdLiG8+rgsAW600G15QUeT9vN8bNHNUDMDrGd6WDJF/mKKNf0dEJsHRrKtCvyOIjVpDpxU3feUncLXgtW6qJye5CmL6k0/s34hxC1tMUALvuVCNvybZrmNhhQ5+htVT5WjO7L6pn00QGEvTZPRHyVVxFMwxIBsmdPkujVyhD/ouD2zt879tq1quEIC6qkkw2VWEq4YAx0Zx3RWoJOQzZc3Lg8yhW6Ynl4ekYTdh5OcawiWbvnD96IeMZ7g6/qKK9oaUOMHyFCdiqy5BAelKtiKou1k+uKATXVQ0OHomiMFrEMsF7IaQLjMUri3L+FnHk2zfc8tfKn5+kXuwK6cSSVtP59M3MVahJKtlDl9gekQCrR6dGYYnbl+A/5AACgAOAAAAIAQG/5OAgICAgICAgICAgICAgICAgID/kAAKAA8AAAH7BAb/k4CAgND8VoFB6+DDyTQEOS1beY886eNPHpVQ7Xy2BWN5NLpBDwxNLZg7+sqnwmVX8znAH1rt7tdQrlj37lazhXRobIlUilKN1xv6xOx+HvQ43MWIsI95WLwSq6lGFIiM3wWOBoylrqx7Y9Z5CL3hHL/+3crAgkW2MTzE3jG/qECHwPGU+WtYA+KsNFRfl7KUdxGAgLIFA8OOakB+5Cme7xcwhgVtzlbDlgW64VZid3BlmiHBEp8s5BJuhSlZ6hp7eaKQMtnnyu63ZddWeXOXY2x1gzS9MkjRCN6DgICtGLUFWACg13ti2qxoJjKfJdHmcS/BjISoJweTft3bLKkR2GqIbI1GdErojii+i2vYs+XpJ0EUl8zyvD9924yhVUEfGgSCdY5J1HdvR75+3CEV/xCm5RRTowTPfJ2/F0g3seyUxBS0kaGAgIJgYUARI2LQMVg96hoRW7S+aCTcS2+Be/ITdq/tRvFYA53NxHRqPDD5JS3Izsn+ndAe6gCCbx2Nc3V58WxyukExz8KBQNbd7JwGuNpubkT6s21KrWyNaEd/DM2sMuQzcJtAoeaGNypoWbTd0E4vq+YTbHw7vPdQjHhYba6HQ0zSxRnk1YCjAgwAfM+GfratwuOegc9N7O98zKYh3RQNEfE0DrBWgID/kAAKABAAABJgBAb/k6Q6l22kPacAwh9LZtejVXieEwIfsVe5NpQExEMb18EQQGD2LX1ZnvCea7MgeMS3Thq3Gnl70w1IcMjVj8N+xxrkG+FbtZF4hyvPlF8J+cwMgXyif9r16CnMxJgiXswhmL/5sASaNlKcIFLfo5BKutHMOeqlERz9gIDh+Hdbu0XrK90je1e7V/BSPgY7urXabtH/C1+A0gie9pA31vxL0J0E6aenP93ZgvBCpzyDmuBo7myncHWmzl+aEXYOgVO3bqLMF3d9NGqBzC91zJtHsZ7OSbAObty/PGAVXTVwVFPVX4Sy4vJTm0al+sE9Eo13E/9MtwzMwwHKHeAEG/x4K3EzZRODL/mrh9ITG8nikz4nM9Q3UB0DMyqhlLkeoeyDrSkp8uJLm+gDMbPg+Ctl0taMusQc/q3Euw3leFkAkATaZdjE+gzVIfk6WEF5fBzootFhJOC+0lQ2Y7wZJziAd+qRsC7QmEzHkxwkQLPJygjfcC2HBvAPu/Qs43Jh3FHS/kT+2liHzewWshw7jRqIlzBjnsHSHHdrW/cR3YMD+7D2fHaOeiF3i2+Qv3ZQi/2r7yTGK64NbdiCqMN99DqPTFMOrcXP+CLJwy108akmi5JgJXD2anDhsLQkMDaBV4hXxPBIhsBmPWTOySDC8QFRu3iOX5v+6ffy88EoqwlGwaIyGEIHqTsp7R17kMWZmJb2KDuvyaaXPcc4dnIKacJuqfTH/21EkzndKvX2MTTp+u5v0mNdxweV5tzAd0ciRcncf2o61A6aetDrQIB1xddq/N9merXsafmIdfhLMwZCIHvIWgFgLlSnDPm7ihM90LbNGKgU9qC032JMO0nTjmmpxLdgwYoWKCneK7Mw86GKN4z6Ty6JatoMYHbydJYyyZEI/rF8zWOipLGQsxygK0OgSIbRxNGJKOc8g+AY1k1vvJAO4lK5mZYHHiKnDt5dvafZlAjv7wCssD21jtEtZNJywBgx7LXRPspjTV9/hMQf8Qmegg28jqY/A1qXwRB3PETxwZIIxHQZuSP8Zip4vT70NmU4fjEZ087NC+FeemfHhktQ0+XQZHL5Yf7lN07BmfcBobsmffcVXm6eZbMjh4fds+EGz12Ak0t+SvNkSQfPyWq7/FFZZF4cjess7z1Zysl/q6d7lpt27bDFJnvKt71giP4weAeI9q2c3RjMbZFAlVoP7pwoHY77Z4I5g6kBpP8V5DPa1AT7kqjYLzN9PlEvICynS9n5MMOBE9NTS6PHU/8ItkO6IMR7PZOLzFPIl7cLpUBCF82AAfIw3kREYNewDJaFWctZrE6fcTmMgq4Lqhucme/mXkcuNF/ucJaYJBVwtzGJXouoZOvQ8DZoNHjOgdDUAWDXrJ/TEcZjjhBnFmzTXCxJ/SqFCX+DVt3LB0bSRbNphIZKrt5jSbxwt7RGP4366fRdxlXFL5c5G9x+8L9S1WA6VgFrIeByUUotJSTCJ6ItSn/8CTFf2PBdvpWYF3sFaHIdWBDoDB0apL+NsTGTnWQS+fhidMBogezRrWG2zKMPz/wxV5PBK2G9Ub/hRagex7wiaxKSDcg2mEPS9yRM2vYWpaRlECjXnQSuk0Uz7NGftIMkxlai1fo33TAOs4JOjCsxb7FZ9apkxEktjKJzpdAnlZMaobdxRiyuM1fEYCE4nfmAgPNFc0Pk8V5DqCOl+XDmgx2PAHyKRtLYD8LIZn+YjKIVwkWxZfIcAvc/WWqH+NUPbORVkxds7DukTX+fW1x7mxNHgibyCU9oo0J4H08BEVhyWxeUhcDF1oVZGdPBF8NvxDsZm7SwQhzmHnfMdPTRQzQzjEa7sE616CJ2bfflsSveSG9R4lFVoFUKWp7x0Eg7riSRhV6CsPCcNkF9iLtYqevnh1foL9ejDd4kUjgIbPAQog3tC9Woch9Xwlv3paL8e8+pptz9MrvoQDCaVBSsRP7gq/kOYxDnOl8mlIuIuBA1bHw99ZZvUGaeqFuYl4rdxzhRDV66aOamDjEoSj5TlN15hofG+Nquk3MYV7Vl7N0kGV2caNYQmdk/KGQRiZJfPSrSOaJCoBXrgVH7g0PxjdSLDEj7aL0kvi8ZrQrvIDoKlS4QVbqOeSi2PPOBywcQrN1gwilKMA1WhQpnODjT4nbJN7Cy/fdbRX5M2LPEEC8Qlfl99mHG47t6AmcI9LnlmCP3GY5AUYoku4mQUdeGCcreZ9Fhe4VFddrUiHFtDJkzMiVz7QxEy81H4XcoOLXRdOnPwMWj199QO81nFXS3iZF6se431ek89tug2E7CVadMlTv2pethd34XVa7AhjJQQxMWYh/df+8f+zXJneBejvtnb4hfKE3Mtbh2eKvDhB2ZKX/6yd/2eNVtfR5+TCHcLUY+A3DAec7Cbe0/k6Q8f5nRIuyJaJnfBOGaWXBOMc9l9qhUxYXWx/9I4luD3+A8GUv6hd+2GCDny1Siu1d2TxQQ7HfcT8c2XrC49f0HcTNYvsHwwGJz4/l3DjMi39euiVryDgUHp+ceP4eLvX89y88BidlyUQfRZIvQFBoGxl0aIed1f+9UpRrjmT6QUH1mwY07OlzQPGkQPbWIHFDgWSFD/tiYriEsDxJx0UGWsq8h4uFkmLA40fpndk5x4ivFGHkNIwoYVp/bdONj8o85LMCTaiM06d8uDuktx2dlWNLdan2dKtA8M8bED2jA4OYaFA4hgaQyadToaCYl8EtlsttTMU3ILiMGCQvBco5sD6CjzeeTY5QQsqyoPdgaD0Ww0sjSzfNNoKiMyxeRquxMJjTo2kaAqEIJTlh8yihRCg2YphMGVKZeovRTymEW73oWYFrWtgho1arGXY5o41nC6lJEomoEnrY5+3sJs4DB5vSu9pBxtCr+WxUt2YyBQkmE5ieH5h5eszKnWdK3B3As8/YTFOldywAh0fy1YGsjow2FFIWnOGq9tkdc36fcuW30ExJQl1V14Q8tT7aDwvG4kHWt/mg0F2QO2jZ1hxSnDSUt4MyTuii/wQLjdW9F5vqhU2mGbngVmQclj8D8u1ktQO+xkwCH3Db9JtPyP9/LmWX9UhK5vdMxw2O4xtcsIIkFVOu0zU03TIsKyDPFKzbTJaYGrIJlZLznYYoCRVzomDx+SA8fUkA1QUTQ0+UXkQ3UGq/1vPN4HUv8ww62NnBic+9U/IL4PPWBCnKyfOPljzP/dru51lnaDn8rT7W4o5ICxJb6ggKBZzEuqCZ2LknN1vRhjAVLP3HYgU4TGFuvnI3gLRpZOYp5k0NIU7V4r/EehP8j60A33g13CTY8T5VF0tXhougr5xxkd4AZ5MjfcvqRMA78wMEQMEYTSM8wSb/46+iNGjdDnP0/tAKOmCKcKbGygLZScDK+KuMQEWcivxoDwsSkVEjjjWSMwjNS1F011jejau55acBuGsPzRedn9ZsEE5LOfZN/owS3hXYgYUrmO2zbV/8Y0rwhn/RywSKuzevlzY156a1oPGi6GgAo6dSPF0BfwWnoB9aLOF0FemmvPI82se8QpAqgq9PIoJ1hae1R9A4gFCnog8YOiMtM3vKc2NRLTWQxydA/Ib7tthuwG6uKK67EAwBe80f21v08cgbu0MIKJlqNNxAS0YnrDeDmU3O5K99bdiWLIxsd2/Y98aNuE3ZJxpQemF8gxhkUh3tZOUvmizJjPoF0ddNUkmDPJkVKO8lzLWuLt5sGnT0p8ztzoQhCAaO9pUxKYrzuNXQjblzu8SYm6aFNtUtjTjGAUBqQi7YpFMyLoW294bRiapRislQVbtjXUeh/C8V5k4t1sLoaP3mAMTe03gbzrywtw1o9EOBE2/dN31r+V4pgm73lXLoZjlCuzl98Nl9LQgei5U3qRPBKxzY5Xd2zMc20kzuIaOmldMJlo1NrLzs1dVOt9EG95In7FmQy77KXWNnKI5dE659LLiNQ+IKPEmpHbiI/0pEk5vCdQYedVQkBznvjG+0py0JU1Yzjz9Op0Ew+IBUPo0P404OvrbgiuMqV3lYGceCNdXfzcLxSIXf6uBnHcRlRL604UAF0I2Meo3ATS5tDvSGHAS6NmZXUOFZsyvhRq0wYsbLMresV3hcFB0nVy5sT5uDb8YsJ/JUTT0kQPzUgnNWPB2TcsRHZ4loqrwve13WbBuMXOkwVxTEWF0LaQDZTAZGvTPo+5fRd9DULLUQJjowBuFndqzqfCXLGExtaXTv4qubOc972NnUDL5YbZFK/x0KBEQcblbs09PtsRPPSWWNmIC685JpsKg4ZWZ2MU8Tc3uWl0y5VRNy9lW0OsAPAX/Z46ZiD/g/mVTkE7Xt4fw8idUyGVlo7SsoyQQ2CLvqoQ6gG8Eq4MdGoQaX3uljqeOQ2I6omCn4F/Q9O/nDxkZbnmF6PGgSB5b2A73RLR6pWjqtAKS+AVfyG+x29J3tJuAqm/cy07lqirnUA6xIah63CBEpUzPNzJVtddW5QNYKCFHxu8EpNc83cJFsvjgDtKALvutc3Ptz7EsEzPvLlzkLk2CbgveyZYXe1me0/Nt56neojXgqLZ17UfaouJtReE4KXGvjKwjtlB2TGBGhXbpSySLUFEhhlWV3TVxFq8Le2gshEf2KIBD8+1Gho+QgEH2cqpv4fkpdYkERzGjpN/VFU+Q+id+4eRHq1OnTcQ08lvoX2Qk9IsxxH2o42JbzqRxDVAnmN1UQqry7g8SyAEWKCWi0K6KPHkZfoBPqLQMMye2xfXMgB3CqOzfRxukQwqoTcs807lYuVdQjyuJ9o2Oa6zsOzDOi9eSg0IlHvntD4jWudYR4y3+cQzJBrw5KH5gKxD7A9Q7O0XBf/OMs8ligS1GJZAesj32uMmVkpk9oA/GPeg0sKn+pFxVhbAYYassjVph9ElhXd9qydy6ZCsrMDHpDfyhlhi+GGt6VowEdDtSSvsZ1ynR79/rk2K8ciCPzvyABEPthTi/nIGoRXEpX2tPSSPdY73RoVjdmdfsigmH4VyZi8jibCmXxeiOr4uj805aZsW8DLq7nXc9Ji7C3egVzuFh5jkRdEcM1XTLKW/vJ6ir+PiYPPRGPNxhlUCjdoWy9NSMOKCP6c5mVMVEiHgS/UmRtmmMPTxqC4x42QrU5wDOjnh4wLYGjBViHvBLJG7cjbohekBR0o+BFeqMckP3EJ37M7qEov2Nx3boeCc44BWcm0sKmtRWHbxoWm7JlJImprZYmLLi/2A4Uw7YLmRQZbGl4GAYAuKfwZ3iaPjhaDFT+ElmK81sRT/plhULaxE2yhO7Ur2l3XxjNHEwShP9YPCJcxlA58DO+hWZOpfJkAots6J+LTUe3ADnoIvyuDO4lhRw8vY2R42yBtZNOdxCZZw8Uk01hMcfgWT4SN5ESH2IWBbOUAwMjnUmkLyJNamsRsGecxBsTdLWHSQL6Lp3wF2pTZh+7bI6YunXXY3UwrpmJVmIT6wDadKRRyozlX0z/x4zCPGuRKvuUWGf5DAgGflPRNDkj2OY0kknVkJYKFyez9JgKnUJbCBbKaP0O42Yrv+6bx0dx1+M58fyBwM8/K5H6/CleIsWgYpLs29d2q3LHiJ+GHcUW8y0Kdq7/B/Gt/igcXB3IIyY8gowED1EYrwo0HPQLZYcyx5qWRNc37evoeAxtXzlp9eKpBMC8z1I6LxQIa7cDALpzbhDz6DmnzDrf7RMBlgfQJz1cx4UX7T+GmpsgjrpPzcaJ3HHbIRVTYIFBcmNhIyIHsgJ6a7dBdjwrP7fS2zafmE7fTA3OuLsbMDEa1Lx6d7jHVJVsMYhNofx9tLKu/B6b1g1EzerUXdPi+DkoEvh0PgKiecPm5dDLoaMzkqNif4h7wXwyA5r12RMB3b8bVdNFcK+6iyKE7iz9qPYtquJk3c651LMYriH2CUty1diBe73mKueRFZ5itytwwPt0jkChyyHnLqJbBopISHMWbeCZn7qpdg/wun9QRnTLUdeYnGjdewNa0irbJZ340jwVCQU0jFV1hfLzgqbbBFHmxN98sb82w3AQ/2+Za16TIaRGhxnJLKtp23Ri0ZVDDe1C5K0qqKsEiF/1g15jXvS8VYLSv/CXgOSjQfLzObDs+g8Ffgafs4gnRom+QeM+dddx47Wt+L56afJ8wKD08Cbbx9gxRwtl7hhtJY+9xwcq8WaCtMFSwhfQu83MqY0UJkKp7to9BvqX9n915zkfRZW8c5mzMd4P4OJ3sG7pEofT83SI0E6C6noZCnXPZII//T/sz4lE/7KEmvAMpJY+I3n+jjFlzMovKm/KxdDL/kAAKABEAABQnBAb/k9DtF5V8pSPnLulRDy+AzD8FXPENffluRf4ns32WAogIJADJsnTU1NaHT/2+mmiZxonZ9fyBdtuhK9Ey5ndpOGkZetG7VydizBO9lUcGA5CNWNijLaY0R9YaqxXQ2sqS3il+nMzIJ0kDVn2vj1rDAaIv2fuqn9B0P3auho1KZyLcsJMIeeuisOYh9Rf/GR3GTFFJvIzhlrbT8DUGIzyZvpUNV4RS7Xmhca7JqSHabQzE802/RVGbUN2lFGjHaYkm3ewIjLGbMN3Z/0LrKHTJwuEJVPSU3MrufBqfX+SAgN3RfLvtC+uN8PSF8NV8G8ccXdDvXv4dk7aQ1DmJzVZkh9vg7DJErEp/mb7cOxNMq4qqWjvG3D17CuTlnmIudE3CXrQGQ2i8axbnsRBON6ai/KmVpcRloDmgXlKoJUYSCehblkpxof5OIsjpwLDtAEKRrUevOTNZnWfH2YINViZmhMXOxSHDB8s2z2pCGiWjkhlPIQ5JLY0wYVR/sJokVqELNw5ZerDC+AgG/x5pO5DH9iEq44YWCe/j12B9w3QRfwmOzyKoUj+mg+3M4br2sk8NMQP/VYeljsjFcV+N4OR+d5IbhLmYKBmjgfXvooNhbdsknmvPDbKdJ+FE36ODFLB6o8a6pI+p4Q2KpSIHD8KpuXsVe9v052sTPARiWDy0Ow4tJrERyuIHsRunD4qF6Pa7kDRH+5HyCjKq3NIIvVNG9OZILVZlkRyHFQaSrWSgtJegYYoqo6vyweb9Ja0oSK/ikPmttMJUfRCSzKYLu69YmGtQATZLXaiuv8iE3Usx+EeEtFFYlFZGJrtoFL5GQr7Pr0HAVAHENxiiUKZ8xzlQXpgqyIbV7jfWC+p/q7tFujuoIPOE+KooBNN843XjwYrcTkmjpt/DI2csAI9wKlVYCwD8x+xgJFk4PbTlKR7w53tyjmqwyydSf9ow41guIEDYXsW9fCoRw9eRVIfymduHFywmHb6OOrAKi8APmONEeJqjTidEhI6tSQQjhQC0Tu4/MMOLOZRQ1ts/fzddLDw/P15Nmeamc4DBTxof/zxIsFvv1yQNeyXKieqicyUfJaR2wHDP9UO9/F3Albupvx0uNoeet3KG43eHlmXpKnffo9JFGn0abZocnQp/YMghtRsSqt9UZquEgd+mR1zVzvocuG6G7IvXBXaiNvKP5DnjcbiCSEyNXi26gJ8scJZyP6Yyh73nJVr0WValKYqHYKRqOfa+JcylgCnoq/FfTa0EMK5bm7fX6opOhQdVoC2xkcbnDPNT/wnTEca9eN6TF1qfi222Qu8jnP80UyR7CsTcupZhPJHTFXioDYbD1yutRbauFpEhGJ9UVoQiTEhtNZsNDTYE5nNXN3lI7S8TL0KNbISxXDwxap3jkv8Krgaf3NZU7ZUjQLrz0WoW6uVOz2+oimCxmEQw5K0lYTcPty+E4w68Od8S9dKVR405F3iWClAkdnQUbL59DU84sT97kkOZzCaf3eku0k6pqPVMNiJ5VM7I1teCoPz8Nu2LucJyrerNBv8BOPuuWCrBTzNDo3Le8060ysqz7L4ggW4ijsNjxPdN2moGART7+mOB91xcyE3nSRRfBJRTnjVYZ3W6LnYpI6pzoFw0w/9i9DiAi8X42H7bZ4WnNYPy1TktfCG1DmEwWnPq4B5Hdb589b6QtLmqyqYSbl4fsKJbkoGVVI4IfzqA2WEgM5kfOI6Qy+r2ECRM57V7729z7AwCsSpB3tpPA5Y02WVSdATs/xbc9MNJt5yxLtaeGHnmFVwkNgnS3u2fAjoJjNAT9S6wVpPUAA7W4FOMAfwK6ZTfB3W1lpUOvtYel4W3JP8TxFyXYQQ5l5ikHhkgZgC3uPBpaBe2fB3JX27yoc0XEsd5RXLTJuUXlVz+onKkDK5gXhNcI+mG0KGRvd+2ommXZuoFNiT+A552yRdcvMpFs/RRPw45QpYWONMb9pppRQauH6menNBIhE2t3+2eyBLC5CG8S/Gq07pHNWLkur0SK/QtbaXjYFqpamB1svsVlj6qvphlGHy9AOG/u759RzQsh/8ne04GCdETodAdTPSHFWvfuPjui7ZVJQzuWSVPiPVvEow6R/knaaxGJDCu4FoqppY1DtxOXtultL3CpTVh8ANyZCjIpUGpmBDT2CNIpl72LTSEeguVczs+wEuXGMIWWlypzyD98yg6KtrGjfKehrOq/1vIkee9s9gP6AQ1H/TsxjTwZJa0h0ehnO+RY4k09W43RGl2gW7gI75c8pKXp9Lop/qTUB8VVzfj8v29VxP6w0ug0bSZ2VtOj0aGF1aD2v3c4KON7LVr9Sw7Pds156grOPiw7qqy7hcoshI2EZlGqxIuGZnAFXI3tDjpLLRBqdrox8cB7uvh3/yii2j+AGeeM75ENGesrNmT/WGmDHlo2iiFp63NRQYafx8TMpciTohKXRvHZD9lRzvvG9Q7BqW9ZuteyqgCHYwdn/OXk/R9jkZKM2CKjO7Dfp4gkzAvUwlQRrfjUjO8qzwNLE9bq1IHEvo7JJj+KspbAtDhHrOht94SioBDQJL4Tu8TDyZHkTP5S2nz6lI7vyG85K9gTb8KbuojFg63QHL+0jF4mq6bPU4mPauFC/2NLHDPacwokEcCluIGEscG5zUE3L6XoeuCyNiL1Jjl56hxrEA1BGzWTOmcQbzpYM7h3S0jqUM9onyNx4G7cLhJINqSKEE/WWq1kPuBoCUbK5zehrqrZ3bCCBt4cVcBkwMDKNX/I9Z88rY83/Sh3n5KU29JBmfzzP6lsGHhcOM/gmqQyQteWIBAVHcOeD30hl3bsPXjtggaqerr4PBiMYkozxsi2Iyfy3i47Wvuiz5vIJ4G6IUOpOhjPtTX47BvwTGOMcXogDPoG3BOVwp/plkOfpysxEAq3XoeMfVAm4M3Z5eZzYWL4MaXxi6uiDrbmp1z+zIA8qO9HlgvocQM2D8Qpi1YtQlJfsMHSAY50ZYzzfZFA07oS/agEP6riRT8G/YaU+B7T8j3QHKY5s2B5Ue1IOCAwSxypUfliOMSDB+f/l+EjnEPRlhxumEEbFTxhZEvFJVm7BnITd6whjrG/owXgFo7J4VQxYJXW7dY7ajIxhJz9T2Yx2K2iGwQvNo2fRrSwQnhP2vxeRfGDm88uuCdFoVTROGEj/8m5Q2rzXLyAb7UfgjIA3RSZtJlzbCah8Jtz1CRf79UtywDxi2BHpPYLTk1Z2G4IC+fYXtg4ToqtTnQmjW962g5Rv96A80R20hee8Pu129Q/VzqEfSYWDEND27i3br3EM2rdDckjRGYeIAMDoOMA6WRddrRN6zOQYAokS6TJ0KXx1gZuRU5wBVt4hQZsrqzy1mvJaIqDxYZC8108aY1Gxa9TF8IpLQKmtP1bWoY6jsugO3xxburgOYx6FVlXVPY6C9ViuQyyB45couEGECGqLDMRdP/eAkONQaQ0LIDKvWK/Rqg2HHf8n+NTphv7spLqUThphyLWMsrRRkSUkXM8t/lJSmDd8tevsd8yQYqp5i7IhFYW4lOtfpDF5YpxV+/7eEzZgxMxnuJ8eX/AK663OHaN84CwPKyFI57Ya4RAEA9zvshnBPGPmVKOtDEFQdM6lmA9tAqJAbCWxxx5unarPnkSzrmuSmEGIlXYko3/4N2HzKWz1bjueq0cUOvt8vevAD2foOt+KtRhcR8rSOhqrkfgELqbp/rYh5Y8MDMbNsM9vbl0Zlp8AedXPi/t4JRHij4XlgIx2Ya1Sq/cXQz0NxJNzPC1cwfeEGTFUBz63n7aAjqallVv7SmGFrmrwtupyiOKL7GyENRB4h5lQ6gtV+kOMFTRFfRYBklrkMYdwQ1n0w2Z8iHdyeQuGxf96bnfEwDIs2d/UIpelPR5gNJewJotnDi4DGF02ImsTxe6wVGOuxawQc+R4B/qqOsLRCAFsdWjp+pwnTPWjfOTxAxonZpd1qz3J4+C4AvuxXcxalwLM+USd+SRwQH3WkFfC4XMrsf284eoYVxquilkmrRNwgiSrr3djCnbDxBdPSyzUywBerZamgCHPtZ1CNzxjdsvENzyP1OeWxzlPePlDR+N4NsurktM7ew8z3PlNEgcs6KLmJqsaH0TFiCNaPqGQoxtTHBvZCKDBOmgHKvoftMtpa69TrES2pIic68BLwnp3nm8Scl/gBYA4tObHyvQUlet1xtljPOqqDcg0X+Q6tJ1tGgs7gbx839z6dAO5qNWoaX9Zl0HUyClHCiCR7r3dJYcyi9rkNIHUWF1TvjG9gS4kyCoCedCj6OtQN9IAwYgDjifPivw4XFKONckfoMbX5lxQArIvSfa6EqN/8NgDP4QXE/Jv3h4MFDv1t7ZT+ffoFONcb3Estu8ZeysFc+WgGsFZLKIckkjXeVHUbCTyJtUY45VggK9IQNXzeE+l4iPLrw6osTasmzdFPOlBqlLwgzEPhdjVvnnpg/CKHkjIYci781WfiDfqSi1pjnTbQ4sdXK5VVpS1yI0y+ReemeUB/iIL/9oJ1TMDm5+BFuUjcSCSNnJwmHNS+0hICX6/TEhXlkuFRtZ6FD3scgn3hqicCMuIv3P7HDVqa4IcGTJtfpZss7aw6GKhuO2gigKcKjhuEiOITLsPhU6R1J14DN4cQ875acRJVXQe17Zu5ceDu+Zw/wiMbo4zFfDhQFs0ug1IljjtRoHhQF76iR6Nb1rINNEl09XwTy7poa8vbIUZyYCi+ikdh5aAV8wFp6Akhn/TV8fh1ZsKT9BFRztH+if6gtIUKH8o+zpF0hLIqgQkmoKKiqEOesWvnhdnQHvhDsUr9jANK63cYAWWl8VN1VILQ30EhMlBU7CyeKTg5UUbsz2JVMOqIMNndmXE27yb4beRfGo+ewekeV4WnYNhMYNDvkUkz5eGsYpHlz0dsCrIF08IV9qFGBV5N0u1T7TguJvUQWvcfoBk1RQNj1+1yUTEHg+qQ4WqWtzxTUihlsy+GqudXrFXM3ZvjhNMShlXF5N2oyIBqEurJBv+JRd/Q7/lW0YwrWst+pszsoLJuEjlA1iPGEFSO4ow/qTPYZqkHNS8g1C1nc1wDXWCfZvZd4RzfKConTJ1Z80YG6zyDXt+4FcaJYGzEZw/uPVsD6djMLuBYM930ng/93rY706rE3nGmSBXitPMS6Le6TlF6lsoiNI1G81MKdxrroF/AVybB/gjfKoXDBpvgGXJGn6R2kt1vVVEJGGGC+XilEuMkMTZH8rkkrGn5AMieikLtEJAxy9g/i6JX2SyuHa5T1TQZ9wALYViLRaas+odMbgCDqztrJQMv6knVTvhJmnJgrQWpkrPCE/NrzUoo7LYfaaeM8Ka9zcTEHjNbJynd4BYeCB4/BdNelEht9iC+yxRbWS/r8S0VUGigtLvSh+ceC9lWO9PelBvuAqCA5YJ/+c2StoC0vzprcawRT6jpVJAXAp5yj7VgbkYfEdomKDvOAyQnmFp7QD6ocmVahUMudOKvYlt3HwmIpiFVnGW7pKm2fCro/L64u6cMGezTLUz/ZwOZOVuzGYH3VatyvRtrC/2fFIkvfJDMfYEZebTYo9bm6WcJbRtTC09SVpmSocatVSqHJ5TXTngQ9RwZe95a2QoJyOch4MM46Z0HETEnsidPcFl7uu837i7Mznv67oRpoORDaY7bIDfLtwris4x/7v0PpGw9Eu6QZU3jq/4FfjNokrOEu67CcEnxumLbXZ2hK4vA03mWCKLu7unU7FARNicIfIPt/qvOeZdhDDRstdLEeSHYe0zqcKGoUBhOaCtHxCzFZifRtZ2UQG23iFWzL17sKIWZWF52vzXiMh5xgsMU3ifGVxxJsSx1D4kk8MzuLZUARgXuqGjQrvMqm4kyqNRUuQrHYz64SMODj7WkG9Zs1u55/nhii5e0CWzAnsoeUC9dNJrBqiO8d0o5XtOwM+rYz/mvN5PRkCsbXn9ToutyVrMu2kKmI9yBSXTR0P/LOM6hGsfRHAxnzhTgx/Hya45J0lGefgOmnjl52xN/BWo8Jo460szdxtWtr2L2QSOssQaEPJTuwBWbhlrtTGIaRXisKijl3B3n9zsC3NjLeD3wbD9RE9feDTNyumJAPYwpauOx8bQWEzp9t9zy5vsQespoLkPUyRWAka+L28alvbo0/in+xaj4UUWgValoPrMB4KnNRk9uLHrdtR9rVu79OpXoWc+r/BnLrqRb8AFq6lYPklA0x/ycIvjFhqO8aABW4zbo+abYkga5i+cNR54To+qFo4lJDzhmTF2JwLzICX23pCc/+SFmpOND0/IwJUIhVejRvIC97PavxurSs3DpA2uxEz+AP2L3415auQBf/iEZ0/N4UzNUNwKOufA8a2DqwHTRTVRqX+dBLzNV1zDQmauuLnKsAe5u0CwpJX67iy2wZ/2woZma1o0NAJ5Y/FRJUP+myvlsEfb65et+4A+Ak+WwNVgNb17OP3ojhWzve6Af8oGk+fTCmxKodd01J6CB57BV6Izzz9yh1H5Rxwd8L5lqJTxbP41a2oRkCyUOqD8Xad5vPp1obekBajvZHHXeav07ytdyjRhzg1vtCzB2vJPfQtg0USpzwBLVFAuaSZkchyIrmZ5MBhd1ngJgWspOWpeHiyDm/N2/Ju179HeMNih/ZGm6bSp1kE6bUOMYoS9h/WNH4UIfTKt+wTpwxSjmUqhjTicu/+SDMPbf2EHqtDl+w505NWWYjS9SLTkbdfd2Q7iKkw/6IOeSzYE4F/FYlwSEVmgctBAAxYtt3yyrYo/NmzrfYad1XZNaGS6G5rBx7ctt/jENuUsqjwz1psPeOdkUhLOMDvo9LYzmHWPxSpAq32E69nB5dubZPWKg69J8tVAQh0IjBDeBSu/+QAAoAEgAABfsEBv+TmHlhnNgwAt9YFcgRoiWML6jq5EvqSv1cw3BHCRjTrGrt6huAgOH4tvLyrC9pb8S12lvhfFHL+QsjDJNw3SuO3MSHJ7jhHlVJOEBKRZlNtOOCS7Y8LWzmMbcaWJyRQoFKDwcUzHPLGI4iqQQ8VXI2dQWcNisrEM75i6hgG46bfDrjDy6anDe+YIPfKJYFcemIISTVK/LC2PWSav6qYbmEX3LxOXjZetbUL76pONLveVlzUaDPF5tbb38Tu6I+P6AA4xbbG34T8vqYOGe/b2uyio0Y27ocjHRsmEcSY2Uxzswt4yPBzYFn42Jheb6HjwBPSWo6MwWwZI9QFy2V+LsUPbUJfwWEWSioW/KA2eeN2sMgWhvETvNO6vMgEugR6Rw3lYJUwftAI5Hydy4XlH6sMFPob/o96x2XRoMthtoPcmcmDaTzf/LoWLRAFPkARrd+Ig5X5hpki22dKCU31CHC6M1tZ+9Hgcfn9RaEfkOM8PdCDQFGvoTVfkwClQX6F289GYyW1le+dnPs1AqNnruXnBh5inlV6mTDkWRlmKcx8RXe9U41FirzcjBVi3RBUf0Fl9mNUsSQzgaJfaKP39NVnFqt+jFZGGA3GhrOvbSu3PMdzy2HDjnIS2NF5tu/n+hbdAdGfTSKq0ur5CHzL3nPwI3jOdsCJqVDbfxIsRxBqJi84lfQ30pglUiAgOUzIMoYrlvAq9+6gNuLhc60c96m/IpuKPuHWy6W6E0KMZ520m9kp83rnWQvbbnPDtAUK0sqDjjGCyHPUi7JCpJoWIMVDEmG33L0GbTvvrsrfEeTyDhwmkVrF9uI1fozQRK40IpbhMcfHXTUtNEJjglMMYuaS/qau7OgCRLMpPCdVunOXMV6EGqTZ+NyIN4LVbzf9oAu+H2pCnbJWhxLVgzRWI3Kkp7A8Y1J8+jPFurK67xG74/qqT/WmtNz6E2I60ovIwT5krp8u7yljiWUd+bF5wF/P4CA2p9Z5SWhCs6UIirrG38a2iTycvP+tHkBa4FciRtkejZ/u7sSY8Kou3t570Dl+iIWym2FMZG2PoNB8oX94PETZQLb6ncmqU78tryXMKi4BeVNVhU9qLJeGHbXSd+erWjAhQLfe3r2jGtMviQ+i/tcq7QANppkb6VCl70pL5GukUlsp4fvjwvi1XyagWVPfb9hq60wDejge45TMqXiFehVQlzTHb1h8l7eRE/FD36NZElK6F+s163KjWz2lCOZep1oskU5R0gfvmAI/vvk6MI/6Ek66BSN7sRMb/O2lTQlDRmb5loi2E9j6nRN/P4A0MEKV7xAta1xR/TA0Phza+Gfi4mUELpV/JXu3irL2jXTvnAJiHegGDlzRT3IJzqIpzVGEqU5VmeEQjiE9BYsoyKUk/HbveTRgICELhHWMgC1tSruBM7N/s/wKrCDTO9USqe3P4HNmArrBpI2RGbQJy3poB3yvULklfJDG7wKM+DvD5U72JrZ4DzZgDs3gghVeiihcc5iq4a6ckr6yzVS4Q9hVq7RHHfMagHQZeMVt6pOA7izGe54tn1oCny+x4DHEeGRC49hgBuAESPo/VWTaF4XHUdduj673Yl+M4QufVxvpFJLm1cK9u3T4OLgLZml4rT0O+m1Nm52q9iGoa7sejdLTLXxvvl2ryRa2COQO7XMn8O3ijcFDVw4REfp2joiM2Yi9U7yeQaVkPZTQEPAeFYFkOPZEhbUtFWq2jEkQaTdBuctO0khlMtnpMsnmAI8Q7g3V1yyIvfF4OHSsg1sXkLf9MCrRG/42jjAZXq7Ae6QADREOr6vntkkIt7moffajf3Ujpm+krdcTQ1yoRTeb5s3nTHAH5O0QujqgE6/oiHCv5E028aYT+P01bQ3/vZBvHNlJLYUv/Lz4YZvVyMEMQkHSGE7ucxxgOaMBlYO65tPtHWYudeMc+62TfDpsT5LYZ6eWIcUKTkdhqkfZc04FBQsNw7spuUwWvE2OBMIo9UXMMXhEY0kt06/fTalVaXdB/eu78LPEEjvL+0KooD/kAAKABMAAAAgBAb/k4CAgICAgICAgICAgICAgICAgP+QAAoAFAAAACAEBv+TgICAgICAgICAgICAgICAgICA/5AACgAVAAAAIAQG/5OAgICAgICAgICAgICAgICAgID/kAAKABYAAAAgBAb/k4CAgICAgICAgICAgICAgICAgP+QAAoAFwAAACAEBv+TgICAgICAgICAgICAgICAgICA/5AACgAYAAAAIAQG/5OAgICAgICAgICAgICAgICAgID/kAAKAAAAAACqBQb/k4CAgICAgICAgMRA+WjTp3Fh7xZ7OsaEdzLv+6VnPfQIOWJPuDxW04CAgIiB9oIiB9qwzHnpV0ZMSifT73CAm7yRPiTNCG3v78boQgXD5ZebzEnbxbnYyL2M5I/hoOmZK3sqLAjpwpZkW/n74jjOiIQo+ml3O4EDgjZR1nAfgICAAAVc3A9BwCx0cUEE4tRpTDrwhbeZP7K7ZaOAgP+QAAoAAQAADxwFBv+TqR5fta/NSADjGwspm8/ZODnSROCjSylAuj7APrLaMJW3TYn41AQg4uvXZK+3E5ylrrl/CqPHTijlh+wNemN3cFLpbJTUCtk1j7SmixAJxey6Rmc6Kwd5ysnM1IOVl8gDsWMFGYkr22CAgIDFB13diDo+ar7fPM/4koCpOo05zGXDvtWc3gerckG5MJRDziWcDQbK8wNZwm+UztFxTRR0kZ29SyCyD5CttcIDo/E4jL5CmXxD48Bo2LgHbynN8wPth8RdEqJ1I1PT3isym9ZLaSvjvW+vjMMgaLxhIA78XEgb24KudcZDIpjowKv4j6Woi5B/hd5FHp35GpXo83vwPd4LLE8Njmd2fj2zrad5qzNh43FypBrDH76AmXpXz+ALoT3W15GhIfIlID/h44cuZusFiR5s0ZqYlvaLAv9Wsh+aUqHOsRqAg3bUUDCP4HbCbjqV0/ZHjIzLvDKIyfSdxPznupm+18slKUcTfVrI7luR8Mf95gbwPyJ7/FosK/OSZr8jxfyuKjGiyz9v3VwQRT7aLgQe/p796dcx7kZZWEeQ1LP0WlnF4bXg5bV2hFbTxXTQ07x7kXIg+dsmhp2ndY4orb8fYlC2X51GsXmIxd7fmpSxJlt36l2vjY8v5pOYYstUjzpB/WUJPzJ9xGUU6Lkc4784BxSOqkTw2CLXlSDg8Kk3wi7PyHzZ+LQz2oK2uMBVgICT2w5cfaz29xL7qA4fqK6njOYyXaROWlEH1K+6B+6f7W+21dTyAOF2+SNxtMJ1/LrV1qQ4Z22wYEKcXr2lKqY4sMhSxdTKMbzFhXSdegDhUMwwxaVa9AMyZ/v/VjVUX2EnxDQX479LuAd6ACftzChY23ZaQObEZ9QnbM6J1jv7IgMC/OvSUSTDRkJVlonI0k0vbbq2bDuR0WF5aF8GApRMR+m3H95B5aS02jmbR2P3y4H2EX1wDFSEa0/Bb+gMH6GbGW8WYVGckSII748z5P4yNaZTnYx5i+3CTG3H6BDYBa5/g0dFnQvgzPi1kYfudKQEvRB3iwNeJAgNrpv86L9ROsCfra7rnalhlV7SCYquWvOP3lJiVkGbAhmRFOF4DXawqsNLK72rzfX/BDHHcJhHEhOnPExAQ06vmqodsv9GpV5zW2EyZSge6oGxbrCb0bJqzzQK2rFmY873GNM7nTdSMicRdfIMDkyLJbgLU0Hq0pjYM/xTyDpcfripwi+fZBjmTz0uil4Y8UVJy1QmZrYYhFYb08/jXuN046h27qmGwhZ6Q1kMV/INFYmMZywqAlr+BW6L+F8OF0/PYtYbUJN0v3lVrlnLdqYcLXBPtNMpGipDtYVb3EIeqO5GJBlLNSgpddGMInFT9u0z9tLf/FdmqUvabQLAgeWXs8PwHqW1Q+fYDmMItm0IsFMyp6FPQZWnjYwaZPJTiFVA22gTCamK/ZKnvkM3Sj6olV9NY2EeNQA8/R3hgJMM0SevShG2AC4adSqpxMPLar+MloY9pJAfNaTyN3sfJYJJaX8sPqDRsjQqdouBHeqKk+MVXEQttNVcPVLQS01fR/UGcpH4Gf9WOpIZOWp4nIeSFUYXB5N5kjpLEGHT/kRBz4e9NAEYq97U09z/euFZLcpJn99nutm8fiB7RzYgeN1guWZ8vMharcfhzZzL7hEQUyg0N9AqJwd2IqgxSbk8vQmjNFbpjbB8gvUOKbgk5RXSRL0S7k24bnSY1g3vq2lt3RLGHsLeUJqE04N4R+TQaW4WUdRBG4nIlNRYBM8MjCq1p0izihO7rI9zRds4++ZT8J3r8/1nMyEpMdyZEsoBPQHruj6cNBv3gTp2lDitGCaInjFBa2vu7TNZfENuHn1kvx+SAPOaY4lkyx2BRfyvQbKogd0A4HFrJzns8tlGqVR+BfyOTdf0hbuML+IE3F347Uho8k+Ao+FXNhazPrTWpqPq6MWo1dW1Nt1aa+B2RGthqVyzyv6o612mynrw45ErvD/DuPrZoG7zzI9fFReJRfvnyEzM6lliPMjuLHJfjsXdGrV5/sw5Rh3yYjJa1pPpg9Jhs+nkSLnqcpgsIbuOLGz5qvwF/YhquBemA2EuYPNoBnjTNXUF2GjyDgfdmz2l/2xT6RPZPftuCzW0yfORRUOLzIvsmFh9Bo0AqbVxlOkYnMt6zD0ANatZ4B8kDcKaLDPkFWTNNO8I1PBzNl5RzVxktCpgz4akgDQNHMpjbiNQGHHMto17kyczX5U1pQ8kx6wz6mvEtIsz5AFwowHczHIahXLfkAeOqkaXz1q+mLTiE2QN7xX/AjGJKCRNc93PhcSCnZDpZVfD+kt7q1X2OJWQb5yKzq+1g2e2lCSg3ooBjDHi+Is+xhZyuKHzsL8DArQVi0nr75i0cAznFdXZ/amnHeqsLEeWGaQ79yLb83Rf6Gg2F9xy5Nj8KSJRdLfE7idHd4YQHXCNyeknZpVBkY4qmnbf9Szovcac/hD60ZG49torQ7GXu+goEPunhX6z5w331TQtan6lO6f3Ik6FmOQnKts1gIGxoxVNFNadBCSmJPlF2ZmecgA+s86x/d0wMCfhfWG/7HzVHSC8GrtIUcHVIo4U31zucqg5clVVk4bCp/9rtjmDCwTeqmRGMzTyC90LZmf8gTVj5QeBJY6TW0Saz/0D2CaOHqqRQtWSMTq6+6Cvyz060X1a4iZgmxj4AC92Qj+u/w9C93U2VL7Rz7CxXAUrYRLlBLtpFOKuB3HTT1ad+IVn06RingLpiIRyX9XajMJJpxT9NgRtuiAsgZ9eji59zTWdbuCzzdsHeuS4xTwskBGNc+l8pwM1n6Bp8K1w6txrHNkaeWyz2gHsyNUsxZkvGy9YzhVGGdiyCi4poR1MtnQWpP75BL7Ia+2JBgINQ7/st0onSuZNFkdvh0YdKrf74VhpPm99rWLr9eEcOAA7We8W38dF1H/rih6sydeY5EMDYPGXdUBDm5MKr2jMSJjaAK2tf07to8QpAiCZtHcLQR/sK24SuDVmCW8XGO4Zh/TM4Sj8bEhFFL1U12EV1FbwNXccRJs4zrMLcwhqBM9z7k5f4mbvtqGExJDHjotxzKh1HnU4veal4CSrg8O/sWEz4qb9nBMiMf3LqZoWTM0X3H+Qn7l8ecWPjmQ1380MPNleGbApqHSTgcX3uniMHb6QVVQmY46R0r8r6tedqpKMnWnTF1q2KcppxZNFZa8STpQQKsficUM9g4a5c+aPBsPPTsvh0AGZKq0sbE2PA6dnOmi6nGHKvZ34PGi5n6FvW1FEdQLDcExxwP7trws/sSBEsm0wGOrLG7NvouDig3C3ZaBNyYeLFnDzy8cMTIL/GHXCa9Ou5DGZ8Pa4J0nuqyov8Vcp4wWjjir7TrIbDSBdAeRRcvK6NEhGHLBsZjYTVuXMprpCCtaF7rSIbJb0YbGX8ozjHA9ROF4UCe4ZjodSf53Kh4iLvzXA419UgDdC0dYbt1Mgq035QudyA3bzViGLx0GD3paSIHbYZi9JsyYeQoCRtJs9Md6NS677fTKjiZyLQIzNxzdJo/BLDJAYN2Lc+hc0Nr/EQPmgAFsLGk4DWi16c+7GAJIZ6NlHs+PxsFNqu+tql9f0vaaKgVPBkFxzDElAkSHgl2GcghMI1UUtxjdOPfZtGUsDh9SYSBhguKVEuV/H8zoa6F2SIundMJiKtpK2kj8X5unuUycbQFV7cvzv/Hr6dHxHy6M0Ur1q+tOSIQe7ai8Wz1PCnfyyu5/NivPKUf7XLZYaOBI6+1FsXKQmieu7RvCa8VJMEzCe+anwIY43B0m3Rnqi8NkmL6qcR8RzcMV2dPzYkAIBHqZrrkrv3Anaygoo3ipCoSvBWVMN7pZylxObcBPUyUplNkqFw9Z5AGkbK9JBfDU2ls3xXp+tKF6FULr1MWtsNBoVCQzc8H7Nv8K4Yptp7eobToykOfklcIuc7txmj7BEi4UzYkRSt97MeEczu0APgUuintj+3oICA56sWguanaGytbNCzgPMNHa3PRPxBrXREIfPXgThtA3F8P70BRsM9fEAcdgOQSfenGi8+AFyWkPMQmQmI6g5x5s1ksZgg5KkhD1diLQmk5DqNRG/+GlTnfp7Dx2vOQe/c+2QcrC3MeZg5MgXY4f6VJhg/RhDVYTqQF5lEWW5alpw8mHC+SiQCtVSK5TBMUo02CBUFDpW1+s0/z3sew/UoSVoYYbDyjuect2zSnNwR8aZ6rOH2iBRSfS/OcJYbNXOBMO+lOs3X355EU1DbNgkxWhfC38neF5rAtz6FrRmaEF1qSReOf20w5X+UhHcxED7rAQRVHjwmVuffUs+363EbjaSg1QNsDaFSDoTksgPj+pqODgcfKbcdsiHdboOi+yv+MPP5XLYnQlhoFjZ+HTvdq2AlmthnK17pz+OVxkkJmji8pctMYfOiFFJIvmpsdrPRup1r3L/UMw8xCMFPdZuxcCvwAz10amwLZDq2IsOIbUyXc4XRxZUyF55EYpxD9Jf5wgQ6GfxDzZghzAA2pZNwwzW6G+7jcbzhwZKallQDFYcHyFLSlWa4LpIBdUAjnSmRQiqcUgnzUNQ0Fw9y0R3N80RdeO2SSa7GeSG3w6MJYpW0Z4wCzidNr0/D7sq1mM0Gi20QIzs9aTnXOww9BkVVyoXaT0TJFMwr+jnITgvCuavpLG6eimnW0wTE9lxzZhobDrSy3k5eVSBz/jny37xNGYBqTZXqGrMB4BbUNtSLojvdT66Vaz6/fQGIVq1FMpUBPdXCwGd0Ym/MD6YmDJGLvNvomAwyNd9uDczPj14IcVh/eSSnoorhoUc06dQKIcpiGaEGWhE2RkdK8Pmiqu38O9fC5uHBRMqtfp4XyRsOyjF0fbDEWLousxh5v0MzDywJazDzamrAfjKMnkeaoFSx3sphWkm5JIMelnqoAW1ho2myQUtahr5nZ5KqN4f/yInwXFrcoux3PyC03Vh7qJF7bAz8aNZf4XdjvBwPJ3bhibNk335NIUny4qojz5t44H21JLz4MR1rd0aljwmX1yCgpxmHwH1HileSgOFdSBCQIdSDVWNe3Ag/VvbSViZC3z8egsqHdDF/QgUD2vG/f3a75iVwHY4D556X/NJ5AlilmBWgSgATfTNUp1g9wdZ0435vKP38kBbqOn/kAAKAAIAABCVBQb/k7CPtMDfs25qWpZFGR+s4jD1LuFTI6H2V6bORrr4hXV80KKjTwPB0n5Oy4CAzDV315+s/Lvwa79p/zRyfhb62vUmq757fb19qqq/WwDDZU07/3lhVRaf4fH9x9CBRcdjmT2e2tJtcID7maZs90FrcGaD5RnON7stv+cIx2fAgW3FfuE+jBlwvx7dMWbtxOXE/s1WRSRC5t8vYtAqdxeK/qgKoksoSheX2GoZppfX5CXjkFDMWA9OuZSh2yF/GBmPY04M8KqANLhfPL6+3MPc5WRy1zvA+MYg17SxRCKpUXP1+oq1EnwtkHXkpBJTT9n3+Aw1bHAbo7Z0I2Jfymd9qTFH4E733g+riHtmBjAddYEXJgu6B7+LcXaCZvOKcAAgyqFEUzaqeUaf2XkedwvZYA5ItIakLo1CmXGQN97yvGoLZEyK6+5rVwRoJBhUa0FcbdPd/W8JgSdvKjLzURU6szGSmx/pw37kjsfW277YtGRNoz3D7DAfOpsVsV/cRaHuPKztQlD2k5G25Eq1ns7FP5PwQPJDCOLlWrj3ebrIZ6FPm4H0jpgOJuD3HL2HmN4skahZGRnSUJOwq+Fx/mgTvFj8ZRtTSvgKARmNVU0A0lZcZ/LUJiNB+HRsDlTFZpuw6I/IRf0Q/hspPM5MV6eqG1ZkIHohWh0mhoFsnOAytuZKyMpARcaVRo90BeGRUiYaFoQKS3hdXmdLqPjaFJ0BZXKbMZnBfcV3NDxZahJK9FiFYHJY66VeRVD1wsYh3eLp8PHzzjwMaAt8FqPlB0XZosnrZrZ4K+wP5ionqias91WbVSz+ZDbRXJ6++uhiixCsEcMDD7ElG0BheU7v3Weh17Ok2UDmALEYqg424T/hXDg2hTXTPOHzMU4lsO1CA32yVvdTAMNu0j8cqdJuZXvqbrzmYREueWp5tU31tRqtoGzG2+HFsKdci7uOxGIDi5rSq4RHGwHbyx0aaA478FJe1sWdl5Z+x07HqkJgcYd3UARfiaAHM5c4ypNzzCT4Ie9iBsf8zrN3aVAcXz72DFGl4jJ8o5LFoBX80YAk1qaDqqDoOMLYQA2IAw8Uao9HzvMdsr0slBGAnf9o/bCoLsaAgMLxHKLUfNHCn8exgNTmZLlbJHRF294ln3WsQat9V/V/bD9vvbYe04Dh0vsrnB2rDfZ0HuXUAT/7GW0lSnvARj39hCMzrgNyAgeBvrXHtNArN4vNIdpSSBLkcg3MxuHdPFxV5ZgUrK8dx1tkuBi2DTbDplTQQ1c8ri1Sk1Wbo7RHZ/REl29yAtuD4+UZcelx7H6YKl6ZgsOhbPYBdrqkvuMvR+p2Wv9edeCzUhF3iKuVl/M2GTc0DJ7hYPI5sZ9e0VVFSAKGLq8vBd1E2axceX6WQJff4U1/0MEOD5oiTCTdhQPwzMc8QoJTlnNTQSBolkLCRZHrarrWvxZdTHetFJ3zJqDfSdu6r3ivkuhI0oH4TZIAkAWz2IL/V0wxhVCXZq8BUnSU0lbFLv1Z15h9F+g0KiXfO9+GVFzs5iKMYz6SBs9pLQvnKvauZlzDBGsdP8P+BfZ2SiKwdKRXF6jL3Iw0isi9TKB/rCX2y8eFseMqfxDyFSHxpvrfP/XHB0xfnH3EgtSDlGRo1KG0HF5LmesK2WUyqBoAftr0IU7sgvsuIBFAvWoMtqGqiSZ+c8LaPVwgs6nB8IRLn7sohCVxSfe4UzO9WYw+8EYECtJcz4rKB0Ja217fO5Lk8PaByHUb748j3hN4ABfPhJiRVgf8aCUFWPuOfFcFwzDe/vvQ6X4euUdu5SAIo9/AB+Ln5ek51TjvjAmwqQ4c1MzV47JqXgztY/WkG7mYc04cEPNy6OVBARI+Wtrw5Yx4uk2AvdS2VooNfm1vhVRl/GmFHU5vVwkByAKNjLAMSQMuj9c7LEYFKYBACjAQhZGucG3GEow+iWrCNHuyR+q14Psync4eBJttbfMXLaAlh4w5aNu/lJki9uWYIuv4Y7sqjzUeJxETH3Ijz/l2MPsJmLvZRKzBXPjMu6l/MDyak9wt7hPu/mzDBOGUYSVwZZT35LU/e+mW2i+aj6nlji1AObEH1MDfxbsTQO955yQHkk3u5i08xA19zB8WEwfFANEHv4bDNIT9nX5z0WsPBMs8E0OISYKptPntoJq1qtodXBq/J5BtPnWo9uKirhItDWbT22tGXL1oANsy8bBbFGLElmZJ+k5VqkERcoNuD8i9bfn3rJ+R36TiC6b37Ot8kMV7Ye9lGDxTIOxWuGCz6b6PHhR6b3Z3nIDUFcESGqYC17dSSO/B0AnnF5GLcM+31WuYjgU60+yDhUGKieAh5HpQ5x+QXr1ohJGoJKMlwqmMeTKukl3zCIYSMtYx9vX/d++8XZbnEFWTV729EkvDu6DwHNgIxmhWevwUWJ9KIHsHM5U2ZPkMcI249uizSD0VsinAllU35pqbb01Kh1GRCmRNn0d1PEJh1fe07pQNsZGWymp+s5Xkm02t6GADvjFW0Vkrhkl+Bd7B8vbiJ6KVGc4QqGa7CrrCkLGTX1MjIlFe59+2SrKS7Lusa5K4UylBWWui1RmhCUE7j0ZEgVD4a5iL03/vQGQ+5623vqW37Fg5XEcn0UxNyG4/0N/0eA1vzVrXZrVwb1ox1Rv038xRLOYolES1T/TWliw99x5vvJv/KjKBu24KN5IaZehH3tH8I3vIEaf7GVecksO45CpE0bt/BUZrBPi4wI3zMLuDhQYmY/93oIpwCFKR33S0enKdCqsLlOAzPY9+F2j+0hn4kQZ0TDdM/S3d5kr8+012tmdzg/2b3J9apbwbbFQ82Be5GMLadVsl59Ic2mnV5TxZ0OlDYqBg6YrlL5/AIZsJTAtPXXQc8DQxPEWT+34h51/eztp21PxD9fl6GkspNSUg2KxgVX/YL50xEbcBfXgr0acrglJrJbJ03WwW0/IJGpwhasePvxHVQvjweRW1K/H/G1b+FwkLkIlKYqIr7AFVdyTqM0Hug4hRPpDa1tSwlMyA7f8OCLgilMtVtrKub6F3TPSoM5M1mFvT9MP8ZWkLSDNs5VWoub5tYQ5FBH9BreVZUxx+zla8b2jeRHaUCO0Y4B0/S+e+4k9ppM+DTuqGFAouJ5W7EAWHjB/Cr2eSOdZhNQX34M5aSL2M3KXvEWGCgbeSf57SUYR+frP/aYDUeoxvkb4m65S2tk2GY0GDGRBaw+9vCOSjlMYmbme+7N/fIFnzDfNsiNX2lCZwFFcxlmZaUdb/OdKc88GWFSSn5rr4RoCts1rFPIgmxRAfqpyb/ng//i1iawNA+Bui68aehnXaieCJiTu5x6ULnlWiRAPKSf2gmIDvBBOJ5EOaLAEvX/CGQH/usPFJ+RgSixzb6kl9D1YQMffM7OzvNVbJCUUojtK4dTXNP7JU7DBbOnODGmNCt/vVfifpdsMA6uSHhF51hP8Hyo6YuCiKt2jMDGllvsaZaiB7X16K4ln9SmZjg0uvSfXIQPm7C5sQRXVz0C0jMo/iSSNbn7MJw804yie95n7SBfbzsiHkHnXogYrAb2u6CEeG5mkgbHbHo24o9T+Sk7hTp/DeK64w8THaThLp2BlD17FpM0VhaziUqfPseFCTL8wsJBAqpLeZ0ehPJM0gllYBt+OhnCgAAIqJK2Ami4nhsT4VxKDhfh+CIGcQ7D+PGrwwgrR4qhWHrULtKgjhX8OGHgnN8XIn3of9hnotNG9EBT0LZ3r5NO1vciZRT6GB6eUWfkOWbZCHTxAXVmVufKsYej7IZ9xMDmccFLQ670Eildx3DwUdVuNo2Air3RW5WaLlQA+hpeT3hfOtEV9U6O8qyfj7J37Oku7drRs5YiRgA0g9xuj8TExrtSL1lnXTSdpAv/tohuVHHVFQMU5f3lvtUeY16w2oPPaJ0smXtHUUzASN20It7wSdsXIZm4qZ0EezuXnRQCZBqOPB9/X6e4gxE9ZJS+nSRBCPIIgVTy/a4FUNcSkmjDmDqSvm7j6Pxw2/NxzQUXqEIyp0YCzVfi7VWVR433XWHsbdsmKnPO5QvrltgtMrpl6m1B3MbendoyCksfdNFI8hAr0Tnyc4vYknQmdWjCxJJmsBMCsmFOZK4ywvvtpiwFJCTqP4JqmGNiFdEEpmYGVGAXLEQw3y5V9SnrVBL4DVNCiXiBoL4rgyLC4nDk3u054OS1dihtuNHAPGta+XarDqzF4DDPgzOdW5EMZiD2rsh8QPouCwZbhFOwF7LvGkDW2S0S2f/zZ7WEVNODklGTHmgEdibHJPKhaADCjMNdoKs4IF7HJz6Ns1euDX/ekIBPhHQKjaxV5oyaOFIilbGyxZxbZUZNp/ML8WBLwz4DWNZwtkNDlumDCU0k6wcBqFXez8H57ZOUIeHcsLahy+cCsZQIpmTSauV+MKJo+UtysbVPo5ReysmAfKVUC+x9yxMhbjEBTmdnOyRHXerKAQJl9poo2eRn8IficQiZzfKkPlgNVi4vEgw2q3MZJzP8lvTa863NBVxkVWQWmLA9IQX26JfQTw3vvoxqI0ICzpJJ4eF1qRq3/QTiTYGy+vSmrAe25+K8invOZykC3WCFIhuTLaEQOmjy7g0RpWHaX/gUfihW5b+G/8jhOBcrGKAEv4r4tUxAz8N8nZu2MQ97eg+GIjXzCE+azEl4CpoasmOQhKpiExl5Cakr4w8SRhk1clIDC4IO6aH/7nLtUrOPFzz/xIkt+0n+tZo1xo+46apfx5bjtj1CEEugeRAtNVolmmY2Knn7QshWNFDWB8l+G/eR+MihIEmHXQRjvsC0lwE3CX/fnSnRn33Ab7/dNOdSo1rUewKR0ZlEOj0lR5iXVjnfVPcLzJ2yXIyf9BrV0e8hTcKotm/n1Tcf+FcBLZlq5HhR5cUoAzsz5FE5U3DK6B4dNv7vzpq45txR4xNR9wKs9T80yNKV5FA6LbhTOyqxRVxAUm9iu+3cF7vzGKE8AgrDYAuI6HeFe05f3OuAs1esivmcYOk3oEaqQNPDLcs89TTCa9XE15YZ7fnB3Y+c+H6YVz0LvqwAjIGNatvhjdtSstRD3wmRkGSJHpnePn+J4Ou/kTCYTlW8UoQj3sR6D+ztSNXE17HYP/PtbZ/xRFRUn6ybZVjs8JekAsugdlEQLHayzkdwxhMd1fxij5XCC6FUocmUHiNxA1Zmkyhb/9WpLoxxGPnBe21PAi6NUcVYMyijJ53Hs/yDSNFWeMTVYwQNBS0brYiYhQHbhDqiKXlD+JSklQ6eYr5YKQ2iFdhJK8S9qGbp11wNS5DFBpLAVg+Yq+gv4eBEeCQ4Yfm58+vzmynShR2C3KfnujO4q4tDKVb7DzQdWWs0BM5djQMfZaZbOLBOmcwAQfb+/6l+y6X9rPws4LrKmuFog7+LSMgyOfsQY8hNntdA0xwscdlvfhjy70eyXtKYKigjM/2vAQath8NjhfkIv2Z6y8e6pzNPnUe8F0H7yBXWHf/4VAUua1FffH/Y2n2drmrjwA7y0bbtw9tIf1BISTEdu4st6LP+VXknhOIJchQtZuxykIchBIZBynYUedPv1DKxL7/myx5PV1QGqmFJwu2tWzfbg62r6TnFZBWIFVATAdSADNIIwijJddRE3aQCquUUQvarYMYO7RSkbDAaN1xF//kAAKAAMAAAH+BQb/k4CAgMiD2nJEHy6AxH6n0WZ0tUjvvWvzur5Sy+HA1PEo5KRuzer1WWSGUp/tiCU1kObWHoG90erAmO5vHhY19FwOH1Y0SATaj4V3FkR9nauAgIINMGrT2lAqAA0wC93sBEcbcc6Ph2NbOJoU6EiSP69eHfH4b9gyuoryrMkdmiRJmHNYGdto8d5EgfN1HpkT5QcrFV1KV9ApdPlKbk787ICAgzwCtgTIrEc1+ykmV8ceDfa7hDBcHc6SiwprONSXp3HSuxf6OyI7tm+NkuC9A8rsYgVJDyAwmHZPB4EOXu7FgRiG0TmPHDgmxv9UfSTvzwMuUT/QFKcbd9W0LGlB9Ixv8phlCzETk/R4YtSJLASs7wp4YKIxE4CAgDDkIBEA/1ZAIAHe22fwIA1/CCzMt7CSH4CAl0wye4cmlQC6mVFLROHYkGg1vo2XHyIFnLmOKvCXKmeILREWtKL+q/fXduLSXAGbWvyUIDwWO4Gv82ZMJS4G2uHuj8jzDOMzRm9fQMDQknVL6SJs/gy8/Eu0rMlmv1iYxMH5hRsBhJB5UHR89NLMDUd80/viBNwT3UK4Zt5dsd1+Jf8BGttH/avn6RsZuJSVUieUiPwkPq0Xs9QFFSquCr//BVSKr2aCrsS2x3WrgaB+kII1vCqjDQh+gID/kAAKAAQAAAAgBQb/k4CAgICAgICAgICAgICAgICAgP+QAAoABQAABVsFBv+TgICAxQ+prlMA4kpWIaO2fYfjPF1PTvEuSh7Wnk5CyhJK3CHdiSneUOcWG6Fv6kiAgKvbCui90KS6XmyTaADUcsGjsmMzfbbxYxJANNt5JhjkSzRcCWP/KWZ9xzG72BxvL15aQcyU29ymeXWvv0B/8zNN3R3/im8677WMsyV0Prqmj1GoCfoLiTzjb6Em9K/igxSpzzg4YJRRUPqTyRmhuoCHa0TTQx6kK9nkX+G+tF+QlCel+F9AOeT2UOfOGFCS6N1vIJ+hhvn8234bdQlXSg3wKlwovhy7GuYmPOEP5zZLPa5w1zFVyl2EGsGsUtgltxCXxD0FjZPPTNt6aNH1VbL0ZvwsBXn+DlwP8GjLWAljWB6B7wTMwf4FCf28FKJQmBiMArdROG3uhupHePNQkfKkOAq1mN4VaoCArULTLUWrWjk50DnQgNS3RA3HpCOKrNlthZTIEop6QFSjZBPJTAa9vqEGy3FzR2s1R3H2AyODgJ03K2xPCOyP6cIMP3JQr9st31U+utV0dbX/EvcK7TyvgI/M/QodPt+hJxWN3qgwbpiNUmXTtwPx9Hp/72huQkivM2b4aP0l5yNfVgEN4GyDTKOetbU0ynadNihwZtUJOlwLyUZCezF6L7wP6WRrMHPi/piXyYkTKg/DJdZyC5d8qRl6pHRpumByH+4HkFQ7OrgVKfregXvWQLXriv1omoMbI6oC1IRhwUHdYA5/S1g3dQvAbApFogRXXaL7lUGPnzrDLZVX2FWTZAxvnbBJbNIkKsgx23+rSDipqt5n4MkRAipqpJfkuEV1UCfE06Bk0drlGdcKI4FBG1B8meoy1WcgIELA/0MZshdHB7OkxkDplTdJip+yYBiRn0NhZe6B1ZFIsNLPZR5XvwnTYXqfAH3OL1Fm6upUoz/4N1FQRRK2TXrZjBGpa3jET+BYs0KrH32Zd7UjvRWCesJt6MCqRvGN4g01CvUZvqLGop+501SbgWBBgICgwbDuI6LH8TnCaPlQPl4Aizc7U46Z0I4aUuHxUMHeC5p623fANgNzxBxtwiQRsipS4qbG0pQdnskXd3eW1Cfl6RHuWe3HR/5iJJ3y5K7AyO4UCuJ2XSU2WstfLYcLW6/4BMLJTlSCtd3K/Nm7W26rI+cgpl3Y5RCD77VPkQYcmiwzD1WNnBD+XMAZC8U0/u0ukj0kUm/Td5qXfPps26PL5BXoh2Cggdw+3kfYIWxQ9msl71aPPK2PujCswbZUM6RyhkjWVURft60kvby9/JDwbcCGTBWa/lFzA6Bx7AI2PnsHlUizkwCmDQzwyYCW3coa7U/MG7CjNWNOG6K1VBBykIIm0qZ0dFG3tcjqntdnnLVzOhweVQ11vRnS9BrwMg++3hVpQIDS/CbUvJtGhivMSFpAsV8S2x2G/Vdg9LRM2niuj6zrTIxdBqlbEBx5mH9Z0OsripK2QuIb3daAgJJo6MZCSj1C1ytxHwqMd+C3Yl2o/x0eTfzWqU3HkRwAQGNrSRZ3NC6+c3lndr7s3nZj4BsfsEU+TLeDyo3eORfq8rVddSECOPmQEeONbj+GhbGJdPcGY+oIzfaGeCrpeJF6EXNkZi7NdInoeB4q9XgEjBICsHhyHSRRsU/tCkvKNYogkpjg01achQbQze+jKnAjrSJdiueV8ykaVVtRDnjZCYwSer/+4ThF9C2RVYI9q2TaXrFbjFlKdbyFevPdIcvqQ7/Muze/Ib7FoFmyNOIDTyLujnoCA9+Wix0nDGaiMYP8lm0ZwWT5mjugZSRgFfnHkgNcYa7ha10feL/2A1dj45AHs32AgP+QAAoABgAAEWcFBv+TgICA6D2w/OlPNz4suubZ80cSH8DgQZEuEbEZlDuPREas0a4l0FQslq0DMX/DDy4sDUlLmk9G7gTNdgdGxG+XdWXCXLeoCREW6Qu301UWK1Xy0abzCUNA11MRYHJiZ9taPK/GBHkXI0iC8dYPqXjPIiPlpEkTEsF1MnFHfp8ECa+lt74NHuRORaGU5g0lFhZNgLe5Er1dfc2wI/yUycx5xvRZeaIFYraBhAh6cCR2xc3BfyVD7Sr/NAX/DKtiZDSNj5RKDHPYK5Al5wDDUlcy5eKxAZTWLZKTbUTembxP2SdlvRv67hvB/4DDL2K52tJ8TKGEr/019Ikz0a6fJO+Pwxg2QNrUjZcDFlIZKI3zl0i1qI9UDEuSMM6A1vAX4sgO1n0RLYLco4soVk4q3Zsdj0wmn2oJ7isV4ICA+6hBxjxVY2p7dvdE4y5Vakr16PwH3tT7Uy7U3aNeGty7qESD6kCB7nK9xU0rtcGMJik/DJ01c6Lsg7ZI2LmKTlI9NNv9q8aZh/D4YdyHFntYHu0QqxyZQxqHdS3du1cNHWoDWPmgzNP8LdxaBqF1Ru3fDKMEs7UJfoP6SCLhRP7SfqksgJGYRow6bOeWic4g6I25oq+14u3GGvsKkRvL2tWxfdyNZRqa4i0LONr/ZgPRh0WLB5hLHHBH40PTVjOTcGZe99AMte0ChJYvDxyPYqzGV8e0afn7MQ51RKN4kKIcbN9tqMnODlulNlLPjon9DHnFwnJ3rGrM1IepmYkoY7qFiQm3UviP1KeeniHqc5NCZBYSoR/CkhjS0IT539y7fAaRqSOhKx33WzC2AFi/5Oa/Xx6JwyYH4kf40gCIYx7XXCXL96brmmCQ9PQGaIAFSpfwWV85KbJZ0c9zr7diS/VTCQk2VdzoaJPSQyCEvfwWiBgHHeMTlwk0X4dLtpDBUWNDjpgWcnSXgdyrH4d+eAXFyd2djn63T5aXiS2SebLw6z+gqYYsKM9XbSqR7iZva2750dJUgkoMhBjblDGU840NpsRv7WsONCtuTxId7sS/CIIwuRT81rli8DJSItPXglutXMztznODEXqrscN/pyyGQUq+yElST1z62E48PFaEieNR9YGszJ9AKMQ4tVrhfJ7jfRUldNSgUVP23fjMFpKDqOw1ZXrXau0S+ouncl+swdTSNfNBSWeipuGmh9xNOXTEXQh0RolM37EEkIh8zZBOdTJvFiSnJRzAsc8ZaMJHARChfctCv+zPMpUAGzR7p3A2KrFbNnGonYYe2hC+ckTaKjWE41KV8Uv4pPVoQLiMB7TbuMJ26qHi2ehubFAgR5fFlhcQhsLf5/RTKHWcHSlXXy+PSLHSVAew4+L63ql5nWXQzplrRnCNBoXVhQ4uqRiInCKYgLLwvD0Sh5UzbLkv1OQ6WJZRr+FwppMtXFqNPZb6l7w//BwUhT+KkLz44VHSVLW1v6CYJcFouOWrgHGhwXh3dQT5gsWgtl/n6D9hswo5F5Q5ELaP+8FkZOBQch4mpcv5f31Tt98u7uLUvICA2tylSOdUqvUltblq0Zuoq51F6gtStFcvmrS50DURrdY2r15oq6meXvKO8s+dIuVG1e31oNBQNi8f5UzZ8zbK9GWnvfE/EE/mMozPob52GWyK5jLTn4Lp23h9JmKOBQ8gbYszrdMX/K8HobG+gRD7gy4vzBefzO4R83piiuJbwhAUB5qnLFNw4GYCLbhMBS322Cbkebf9SisfJ2IF2TtEHFWPcUn9nfC+RwPuRZPbTe9YggefzgjMr98WbWginI0tXkoBvuZLwHryV3RU0QzFkS+aKvCT3RvO36gu9PWmbTXLx2rUZSEideIiUVp9QmL3kKVoBfG0E7jBxYpkRwGHpcxksVL1a3iRHRMqmfcvMNS//x5QV+z7VEhXdY30DUc4Bwj2iyIZGdrt/aKf3667RUhcScE4ft1MxLBBxEGT5zmRNrhkVo4wfqtmXwK1OH9WOoFaVDrNyrZfPzt8LJ55DK6839PIP23oBOS/Qflf76wXkqFESA8Zf+UhyHT5YUlQVBb8D1wCzyzRPo4RJ/cT+IZkDX2PL1NAaQxWy+ETAjX+RzMwov9v5gh8Ci9RxNzYuNvc0LbY+0qVovLG62n6m2xIx51feZfiZ4HQ1lUJwnGv3ZypwOZyzO++aXS1HfBxXqmrJvoY5JzJ2+hLccGjH50FqPD1yvkKKLiaqlg0u2mNeJhgiyqUklF5OGuUsbTwPCsgulNroS8kP21wi8aMtUdudmhyMyr7Yv89pgrHxByiWkPJxC/8VQeiaUTwdGxpvIn6Cr/tuZYTBXG+ZTIkc9NKp/8hxHjFY1lADdZoZqHSO015/Iaxi5xS358LssEvIbmsOPu0q7MK5Rl70hnaIEf/PxCXnoVBr/KJCZUCRkVEDSCxpKXesBXeM3QOvBx8uZ2UEvo2o9PsJ8Q11cs9qCt2ek76r8kaYA04bsgTU8XbU6lZNvJxoSI9CiY9bZdY3t2wLm6pt5qtocVrOQCoh8YMIuXjpggg1aeLIlV9dBKr387G1esGGhS6NXFhYuOAdwls8nIdzIIO27bpRzvhAJOrEF74g35p1oZ6LwD0zOe4JUFfhUOOQ9T8g9rMVvHoiV5N8SaQTG/NKoYlDL9/+rVjeXw4Px89o06qyYcNyA8ZRgLJ8YF2O1jxuTMqwfVsoO6b/DGXy3PVS0sDfYMQxxQkiLDONqV7HjVVUUxOHZNLXvaJu1C2Q4u0ZHjxG2/rHiEPlsR/sHZYbFgt02KiFCHps4K+nENKmoTl7Fjpb/b9G/QrpDA5k54HZf8o8OkLrB9BLiUXxXmmvgS0qrAW4G8i75KNfRWasF8HWMF7L7WGJ9X1NapMkIY91JnbuwHAEeAnm35lEuXkUwUEgUsjPSN3xagkqxRFI8dBvP4hLxcZUppj1zkJ5/OjQcJFGappeDy6bCva4DVl/aJLfIy1P/udhnw2WpAwTOWHNpIMNAGeICidfCaPhYy8frptAmOQeApw8mJ9N3PgIGdw0ihEK6YeQvuS38V2/gLmM8vby2eXY8RzTNv4seJv2lzfss+etPmrRMj7yD3G8Rfl9gY+Lw/5EqkLkgQ8Ak0vKB+oNpwRsv7/RwnNzdjBhXZQAmcg5VSJC/alt+1d2XO2KzG88JxhL0Ms2B561dLDcVw/6OJawvPdqdnDRwIGFxzYCWwt8x+g/NyBXYECe7wk+XgV1UG3TkLHugqpBk3LNcvE+jrVAejXa5xm6Y9Ti8LbgYfWcVsyvqgdGAQWt0OyK84wdFQJod7tsGpTe6d8nXmHIMHB2MYOsnvbvtpdKIP/S1C6MNVDOnR8GowzHH93xG6Q2ksNG7uMWLZRHK/RQ7UOHk03eNfbSYErcfk3I/cX0GemiJ0EuJ6Kpdhscroc5TR5j/8DwE+Pt3YKePUSpLF+duALHe7BEUUm2MZRcKpxPstB+JiKITcwnmmG01ZyV/B2UbpT4w3b8lmNrk4flIBY9t4xubscGl6HnCYmBk3V7dj8PAOwZNdl/sqAgOGfLBhohPG2MGGuoYLCrCwQdxKzTWoy7NMQPdVIPtqtGSs5TMoA6LyyBseClEksPDr/GiWzOVKtt7GXXaqKJMJBYGxRihfDrmSq/OTzQSLzyfKYPoJx1ekTYThafl9g9x9rI4EUtwG4Jzd55yUoeV3zsH24c/xwtK04NsYDP2rmD1974TWLmQV9eJ3x0cQm5PMQ8apcGNxbAED7v3COCozz9nxPQcWAfnszRjk06XCvXvH8fpIV7adXSxonNQ9HjBysJkMXA3HWhhAoBIHaEsbJ4rjAEF+nDvb6xxBNLrwKHfQfVD3UtDwfa/vYm3CrgGIoQsdUdzrGnlGpoaK3rCCP0yyhuG4Tc+hibPKNXudsdcS2G3epl+d/Id0qh1XyN5HnQp0ioJUQS3gdb3bfu0FwlpE89L7LyuR2LBJSUBk+LjgKNKigYkAq8ST2wtwi53PRQ+NrRO9z4W5YgxBs/CHcYG+GX/TWI5r4ULnc0EKCvXtMC0O3xHHsGVrODTTw4qr1vmOvwMLXOJffgZMscuqODI4SClHL037fNvKwsLuPXe6fNVk+uM9+z+R74FQcJh33RqEKVk/GzcZ/EMP29pQLuwCQhQRr4bUFlZadJg8YuK35z2B66QP6I3OUW5/gf72vSqKmokAjasdrGal46WeBh4qB5t6efrNWh/jbSntIa+Dey1ySpwN1PKHmogjA3224r/1muRTV3Jc7O7hWdlduxarKCWcMIkkdf3IIWfqG13ZF7eldHVkXnxvJPvzXe4cJCE4OyzRRWR7nYd244SUAJavJvAVEWl+4qErTpsURp8pbA+f4iIAqTQq2SM5M2UyBWnhTWr12ocvBJUJrGmDHAnCJU56vjW+yuf3DQH/bEZ3YEx0llgVrRO0pgekwUny5hMDCt+MJqBG7unHNBN9j9edjx5ZmjIV4lwfbL9utZBzgQz6VV8uvY2sf0sUW08dzcWpGIc5iHPpAmGwkqHFPWhjoWZPRDskQ8zG8MfxPqySh/YTkoOg7gMvXd9v92aWFODk4nC1OiHzEj5h5G2G+L9CKhFWdM45vinrJGO275p8ItTBSVvC0JZw3CP0ETIhu4ULQ9o2jD2h7kUeTCcADdJ2eGbcMYx4+NzE28VtXaZBxiuDjE6HTN0lUqvz8BWSu6Dqvt584Aa6NjTnaFfGkeUrJJa4vjt66/yqrjSwL6yR5AebfcHXNAvEUnVmscV8TJ5Z/tS/gz9NeoQBZDRvMAJxkaKNPNGz91DONc6zLJ/EAR/DK8N4ewvGrSRkTFULQM/Au62t+L3mGrbgrkujYRIXL6W5SGKSzGZ2o2Oxb+zSKyeHOxuzRAi6oSgKz9gFXN+CuuNiYtv8S+rFqRVANQcPB4nAMGZde7iUyov1kQ3HLi5OBa2kiSDTnLw4CgICJXDpaFWFrQKmCJry6dJZG3VxLCxMq+8UtNUi6iZQAPpf/TFcxWO/X75EIXXXYvFxa5mNiIZynHz+hALud181BI6u2CPNsE4/iJ9t/6dYVKn3xyfokWAQtde5Ui8GxNyihDBBCMHD5Uy+e2nAOXYMlRKY2NF/Jin4cd7F88togrYOCdMdUklvhyd38vIKNLcnUYhSY+n6Z9o9Gq7NAz2obnGlkOeBqYFfZSuo/w5IXosPBwJtbxofGntmNPSmBZl4qa8hCbfZDJTHW/k1Nay3/B2wFdzcPrWDaufPCPzy3W3jd17HHElyJLXJ3N8XeZdPxjiVnUe03S2g4Rb8+wvG9NUq2WYcvy4me43e/IAWe3m1xvuXmeJDaEu2ZHC9jq5XvtGV3vxwjd2wmyCDKgTxwl/eYn5wSr16P2EFYviV9oHuiEYwugKbb2mu7EvKVS8Pi8z+DtBHbjceTFcRgGc4jAgdDxW2/Vx0bpE04RWo95mfhOu7dczAH/H3yRuYvFHCBRf0bHFkr96uliFE562ILMT44S1qmKrAwImaYcA7+waYeNgPeOTkKeBkINyV1TxR9gnqFr+MtXNNv+ZQwDRaCFTWKIkqMvkhgqhJgstXi6oMPfR+2UqqW4LYj/Rbe1+Ghv1B9XtNeYnSP+g2nCAqgcJNG8cGIEhT/dqdcFoj8VbQs2oOb6iyoWYiQzoreChsb3IFahtBSmfc5rD1nYROgwvTYJlvjHGTdLtVHmdMzmfkjxmBHxdHcmB5GX9U9ZPIbX4fSvDsf2Yd6R9Nfzz2mQBQZF+7WJNjQJI2hzzi+j67HiLpemjxFlFdyrqo/g2CPXgk+Xd9aQYiniu5MJm8BX3c3DkJPMq0SAonMX5gEq29jkRIGkQsKPUGPg+M8i1bYDu7AAE2DPcgSrnFvyUDyZrXoGiqEvMTUTLRyzMvv5+2OM5nlTSkYkkdI+p3tBb2HKLtUOlOE9cRdXfmt+ejygP+QAAoABwAAFSsFBv+TgICA2G2z+ta2PT1dbdU0NzQ36iuVPT/V7Ut5UW1HzRvWvCgpo6WRXDvtvD5PeY7bm4X2+nTJcVvK9HXtnhzQG6tPZQ4ULibdtPXJAx05psIfZ6X7mT2F76nSXnJj5kZdCvLnRoB0lnmJ14Vla45nuuerQwizIJOk1sMOEhXxoG87rAP7aq2n6/XGJ2Z/zarrzegg2+RS/tu8qZO2yA4z2m0sJOF31w0vFudZy6757MysfqmQe8qQ3Y/R/ishl801P3S899RRidt/F621WsxNZ5q9BHQd0i7tbhGi6ZJ4SSYzz+xHq9Pe4QFvT8Q2sgTRKLcJ24glGHLI0RKT8ylCQl+XQmf4X7wygYVOQiJS4xrlq+EamZT35boVYqj0nFkisaB42SMuAipqVdnLe+NdbGMyt0Hp2SrGO/MkBhphAwT+/Ro79LFjxux7GSilh27e99L1XUc5EDcEyE4eqDjdw6CpNwyXgsqhElrQoDLZi6PMIQtZ83eazB7it8diVAmbtS40fl6yUPwLsByimUghGSfjiUyruUL06dia61RzaoPlwt067DBNQZT6DSPPio9o/tbATUwwMYPJ9HMNLHajxMgOu/8gsRS3g3h/IJMF+zMvoeKHB0TTgIDXt9mg5QMcM9fBscwwtuObjOWj9081w93DVeC0H2rhrX9LWtUAhiYsuWBXVF6oNXqgloX9wq71hKpGJEUGk8foCq3aj2NQTqSuI/HvQ4Dwc1kO2cyVcgyhiLaM456dybRSlhxij3jPhg8qtXykjreTH0k8oK5cLdXviHjw0CWr2/2xpe/XTJs5ZLrbw7d8sIBL2hsQeemZ4HDLxL//gsyr6LUh2JePjyQ/hojP91gAHtN+UumBHhb9HtUPUdTh3M0nAi3kQxmtt/dWG+Rjas+rGhCdzQLOAfcE88W5z2bfH+n5Mv5tWN5ZOmHajjyF2jy/xVdh3Z0MjigeKM3ehbhVU28HZPfgqDKMuWhhoO9w/WX+3ilgh5D9D5EScI7VOHhuvq/qZ8vcN2P5izEHq5GL/Vtp+Rd0mWvOCCfzar9TPw0hmkkc6ww5gDbJyilyO9SbObF7sSJkIKwP+NspZc1hRhIKdVnwTXY+cgQjMfBHZRsCIkEa4I6cxFLptYVW+V8wZU873R0ZxLgA2aQngjLTfODycDZQ4zwUg3+i4S0216ZseKlgVjQoeU29zAhy2XsxOr3K5Iv6AKsLs58JR8G4AFgTFdId459xSI1lPFvZK6lvzoAXJ+BBgjFFbFGOn6OY99JBraRX8RhMUkeQDACYomrF/v161Mlm5GVZmvnqKZ9fxZJH5Z/3t4Cm1Rr+hBfUGg83VgpgaETToe7Tm3YFW6wd2iyARLpkoQ5+krBmeIu47qoAJ1YzX3srJ36iWZLsg0g1997XbuQ4km/ZQMiUyKxqaEHmInKuDzvm8f9Xq/mP5RjSDc+R5Nf/ASXPhBzLAr66I348CYfYzr7/Od+b2G8VyMgbnAQOxeqk/HOEGGWUw/Zl18pGn3mQzNSoLKjEoY5iKUUUFf2Zy193LPDPJSgrwUW7dt4zLHrKYeTmViTqTes1tVB0/Iqipv539A7tY0vq6FCs8CtTOqV+koVQxjM5zhUhBhJZw99DQ0LPXJw5maZPRYl78dhr7Ni66ZAZ7zHQowOpaZM9jbVxQPiGOJLhu3vYKbSQtrqWyy/HXHrwMeg6ApIHI4X4wEKQmXs38WeSvF7cIrsGxhW+cXnF/1Ve6AK3UEg6n+rFPg85p7Qn4C0L+Bwhl366t5/qVlvAFk3T++9bfZXNBkQkY9n0eiQfPvxHjOWoAqKEIW3N81m99gPqFcWHVZRWwA89xutDHjuIoJSf/GpE0GuWmJnXWF1oMsPh/3cpKueU2I8I/ln9IsSGOqQapWV11n0meWTPO0dleYCArZWs81NbRVF71QtQ5qi2KsEvj5ajtfC45Jzd4jFl0Ttc1NqFa2uXZYuXZz8FfW8AsRVXmo1L9ONRK+qqlXtGueYZtezgkum1UoFQBIQ1MEoi5trRvC3zi11IQQpKfQdNQjTuh9v8dYc24uUUAaVzHXFz+I7IYzlZgMQbEhXm8L0ZXI/KYp1b9r/LV8xtNvxXBAbvNwsTzaK2GVFYRC/xDlKE0BRZZxrHSmImJp0AiLewTyKBW0XE7vfKvQOSkyejzIYHpbtgZmi8aWnv5t1s9oydea4AabffWiiuexy7Mup56YB2hIxTZyt61ziVwavqjUlR9yi2XUsNN5sA+1LVhJ89YjYnFRZrAvl0ooznYHUA5VBYyWOSrAT+Q9BUbYfNnU3YBfDMoXdIZklI8zumCy5J+6Kr46zazWPvOIAvodIznFKNZUvvd2F2PNKGEXJAhWqhMx/R7WlZsMr3INs+BVRsO8BfyhFqYELNgaHzpN+mDXTch48GpUAD0AdWnYSN3k/oJwAdbT1g8H1KDth06vjoiLyZeLCXKHKGAmzEfAuycJyQODs3K0bL/t++b4SnqEsqob/ZhiOzeCM16s6vd+KyvHqjJth3jvqt7/DJ0ghQtNVHKyChEMI93smVT1o05XPoOw8YBdwWNBgcrU+2VDPS2+utzPGRlyhU0UkxMTouSl0Ro300y1bm/R/dzOPgts4SbFWi2+Tp9M/2Alz/eRCaeCIyda2TgssBRVqqIJO4LM/z+QOtIUbbrFodFQ282XitSZJN5qyduGeBrt8X7olEiNR0nq1ZKG/uuThaP2tL02uYVJGYvRBYmi7BJO4SL40Lvwhfbe2Pz+s4yKuXjKCnfjk3JXTyuM4IxMxXYgwEBlYUfWI3vsYOona4sN15Eh5tnZjnCal31/B47gg44J8W9HqBfUpl1ZruXslT8xsV1fpG10j6u0Qy4O5V9vEJVfoI7MgEQVc4TA0n4qWXlEGI01utdkipgntH6Dhy5gtLyIj/deXtmAljqVOohqC0ibVzIgOG3cCy1zjmxu3jHteIo+HuN5O5CpYS6+077Kd7oyTYc23yOa8GPNfqx3aXIEvytOkCa0GwF+JqHER3FFgLavmjf81JeWaeHTSZld2DsqvvI0GkUWazIIe6TGPSiGD8nP4bIug5KIpfI569cJ8TI9EgPUhJg0pVpD269MpS5+ykocJ9XIbU+RHKV0kdMIkpUKtqWmq2tzCQMOKfaYJq3BnN5pgXEO6zBdB45QHpG0LIQQze5/8mtzX6knYjDxnHVuf73ohlKJqy7MV7+HyJmKXxaaeOHgOwY/IDAwhOQTvYsJ3UGOpstNcFsMfJ6Hz3FXf7MSleu8HAnDxmer59sfEgSJDHUFmsykpTfraRdrSYcLIHpJSHcFM10zbeQxfmTILipMk6yehL0EjQXa0wDGR72yNfyYx1WfcGVRoBcYM73e7KvYi5lMYke41xXXVWkPHJKmBCvqc5BVv/VMartMGr0MnkXKsr96AcOvoj0jnsgq8Es4rJO2T6HUSfd7f4j932EUFWlm2zsaMEh9bn3AAgQGSCo8uoSMYND5SFC0LdLQ+sXssFQ4dGGPtQbV2H/IA4AM+k0vKV5FGD8AnOVJ7M86vKlPh2zQuCuApJJ5/sV96T5/8fMmWW+EIvUphpQVjKpQm4pDeu2bw4gauLSNibmGr8VSerWOE0TWi5tYrwwJYPQJVFKW2NsWAYjqnmFvu/CZY8p8CEN+l6X+u/mecf16EaB3JbkWEAUrwx4GoMHFEFtgZB79I4OCo176hXOpkJs8Wz6HbGyoMUmPOjI4+ewyjb5v7oYKv7/k1vN6cI0GkMsd20q1lCzTHXDKTeHq7SB5+5tzy+dd3TWpW4Xw6NlO2uu2ajvV30ceyJgarCNGkirC3OUkPlERlmOz0gRM7T6B3kmclCC2u8/stO2ifuf0j34bro9QrXOpmemlNhTTb8bJLqVxjkPz3D7YxMMnLOCLTD0Gvb9m+LFGRZe96XNVEXcFpa5bCOMdUg/UAuHWhWSa/QQQtRiKmWLdMucwVxA9sttrRijQkiYBt5o33tRbC2npuNQ5EkL91P2HpjyuGVOYd/7ffSl6A1E+sdUre/443RnWVCL03m0t3wibvRNvmQQMA2fKQSgqulwgDbN9ILQpZDajMYFJpq7aNJZvSk/LkinxJv1BXMqt9kdtZPORdo4NfFzAta2WD9M1MJl77ci5YkX+mfwtkhPsDYRI6ovjd2YQ2MpGDND6JfVqQZQjaFpNv1vIrKe3xro+BbnWgkS+GAgJhrk1juGxr+D3b6D2lwlGYgQ/C2p03zSGCXHIFs9QMivaO6MLcco4AF3RCsVMEref2AWl9NEMcBCelWxy938XbnmH3V1GHIGSJk+Kf4kqDvptcJA3Xj06U8p+b4cTAOw2NOjId/VGxIGsUx7on/VQifUvjGBHQB/yGnB/PfNzOvP7Vt2UYZz+FJ924OZRvvgRykHcukFFDCCpO4j+N0RfCQYv6tS/SJQOKx5OYT3tTicUgA+jsYukZYa186UDwcVF0Xcup7191SpH1nv6Mea8WqBmBLo+OjkpySzGiNRG6CSJh8pHJC+n1yxacsDZs8iGH4OTiBsHpEDyPpgaFbSkJ1p06/2dd2RlQbSOhELJNjlhLPIPhNCGVY+cRqgzMmYQ1M8WruGjN/4VXnsDFOeNGAY6/h1xAlryg9Knq+T7XL1ot+h3+ML8E/DnfSxsCz5DddRc6sSio2BDofCgF96onJltBrSVudxSPSswWMBIaxrs0q8fyydHt0R5RNSHwkzcStbvqj9rV9kTWSv+DZD6qfvoXZjPmGZ5pDNXXFbt0hmF7s8wXI6xhwnSwD0M6MT3al3Krd9bRBRUQ+r0RSMIRah5D2LwjPkuryFlFxvaJHHYAM2mPZQumW7aFrKyNCKcrOQb0iDdtxVHyKJUkvWDJIy/W69g4NpPCXyJgH6IbqiIQfjrSgUOSArzKqIeqo9eyTYFhbAiyg+l8QoAFkNl+J5zGQLzSXs9j8Hqc36iO52uvdvCzmjS3kPTYPb58lSshimw5MGQsjOWmNSWVIcZO8sPGiDNxdPbpgNCKnDnKQS+iUch0QG0ffQtMoXXtdDVeBsTCrA9VDr4ddIyxyoy0XbLLIU9if7gMoExb9Cwp0mSv/NW+rygitQelD2f8UE/iZZ8gZJcadPlqv3o4QuhEdoJHj3uIVguq05b3zQvNBip8ljqpWlwhEtT8R4w23hidNTdMEAIgtEwX7GERJeSyxwHiOIsoG9ba64eBcU5ZwfH5qcEMf9hDJh5wREvjWcUlwZXUO/a4VrUryhSPvr6gaiguXz9UHuBHHQDcNnQG4R40ZnBSKd+rYdw8VYkhcAAmkXxvxEhIIxigG8Pjqe9UO/y36scXM5fl7d7PkZ08NmakFHRNfWwmzfgz/Oxz6vIeMS2mRV3NPrSTxKERbv3eEpNTDb8Da078OQvt9C+FYV5kHzsTcph4osU/n7nKztupCbPhLHs88M2wOvcPnPR53njALt8ofrjqISJFfD78CUPs420St7NURImJyfg6CQkO7qj3oVrtERJITUpDYsTgUr/IzpB/kZiRFvfrsdIkVht8laZ/tTIprDYEWPpBwPoOe2wdAt+g6+rbecGu71VoDjC4XTXJKwK/dftU39J07s7OBLiT/CEdx333grI7A1oaa0oezoNDmUp4l1REc/UtA4cAlSbBVl9qFnE69Cno+hf97pnnin0H3SMD7TkBP7LZntEYxIau9HrgZpS/5pWXJklwZ49HDYHQJtifGSq4+5MYpkrFmm6TslY1PuyY5ss7wApLYgrMjz8OTsXpmCCPSDYbelg5LO4+26SCt+yV1YK6mA7js2B928GgfKCvTvKO3YgDEH0dfXm8mJPM3bRN73RIR9RhpwXHAcvG2PsNOl6EGrHvIoqQBApHZOxBPD/e3X6I3Mf8xYNaCkZK5sTojbd+ftLzHiGezbMxreD5yVZkUacymW1kJ3G+sqHEhMPLIk2uZICAxLN34TqUubcbodUjgg2QiUnztwm4jEWMh9Ts+As4+ZPxEZ5d3f4sYIQ6tW/9mBHXb9QzgP1qxhqs8yAwC8e1AxtSKC1PXonFDeZyoe+AGy/GlOdoOeIG327sEdjKpMmXSgkN9r5FrLoIIHchjVv7WfZD+ecpjCV5U5VIZwiewYjsyyQ8YXSIUBaEBVdlvuZY/cktVQBBC0q1r5R9wYGNi1hoxwthunkxnQRW7tA+HAOIjOgKbwcSxWASlNEkCqCRA1IqhjgGWgOx/tNu4wRiDAFfLg5Q8kYh9S+Mmnrb2UIudLGY6xWTUl3gF+t6YwXZex/qNQ/z3TvndJI/nHoxCjCNlUMHGr0RFxuViyr70XG0y/CpiPrgnnjTOBZ4BuIlSTA7MrYhUGidw32hRwoXL1V2ybdNrjJuF3SfN6pKzsl+M1zS+p2sUHvAQvlsMyq2Yg3UOBCpxyb8XFj29EUwTIkzzTmtFUnTfO6wvbxbCAsTBzvcm1WVTG48qXG+9obhT87cNVeyHF6+E/yuZ7aZ/hmJaxS6DbBszgs9mcKuspXUeCw2j5mXboKG4qKKZ1AdiQPgJ91vfhRSK5qWFSyge91XP4FC/YilbuSHipyrUWMNw5Bj2MQM5/zWa+mkk8+yX4DbCrp7xIGsEIp2IT4TGt0XLWIlB4YTyHFywg5anaARIFkHiX6x8MGDZ5uWBTBFofgGELZwhqbFg8kEeWSash4HsD7tD4ZN8w5rJ/jojLV+9WJjp58zl4CozxiQ55QVyeP48KNbpVaJXfivSgBoDtYAsfbEWcGvH6HuPuiK5jcFDh4rfFappCd3H40d074fIlltWYssNJnR8X9wSwO0gSxWY3YZugfUVRrfbZYYiYNMoi4fr7RyX48Dtx8FuT5W7vOqOYgttNu0qvF0q76cOjLowRIE0WWkMuvIkfD6HToAK4siE1Z5Ezf8NMLQs/RXeh9lRFRl/GViuRxacqLOamwbJWGPz2s1gDCP5ye4B2x5MjkrxaCsLkemf1EcQeeTzQZ1nL0cCFkQubjkwUXjgZpuN3wkXsFC/kISNid96JxcJlZr1HB/YH2iB7VWkeujPD8EJnmedY2AxRe67rTWj9uagSgcrAWvyHU3Q6Aa57hemrIKk/BLWjEulgobMS4abTKc7tmAb5d0RM0kicG7ILIOxhhCwYSV6nJFnWUVIINd4e2Wn5voNoXgnYMxXO0cJswicRtwh80Pj6P0ItrpaNJaqPAZct9uvlkb/kAAKAAgAAAs5BQb/k4CAgPDW7Wi/Bqltj3Nl6vK27aD9TuD1gCYPlQBQTCtmWt4KjUf4d5GO6nVdeJ3N5fLnjACaYzTTD69VAvery3hXOancMYnH4SPTg/krwUA3PI6jsWu8h6J2igrE1t8yKvJt7wdd+bcCE8zshVMIExNl9/Z1vUKSgA4djSdmpaS6Y1M8Mhx37QMtCRK6JCI2R+68MpTY/WhP3pv5Q2KnUGJG9cz1J3dnH1IeXio4d8yxAkkC/nttQj0JFCymPJkxqFYmrkhOyK9bOZ83qEQ5r0QLNeSYwpGUqwPcI4kGcFdYWhkNtFinmRQpixcOHWanVgaGoapDH/yg8dTMdRaJssyj0rwaibrsonyQfP0GrlBM4wUHGcVS1BR18TUVJDrKfjEwTIGcAge1J+vy5TDCLbqaw6ODbS5ehj4WPcWzzzpfO6VFV977Nl/t+YFjEjEucU8YaNfk4jVnwXB4KJcxQEUcPf9j0ZQkyWAmD1ExsK2I8AyYCYlkuDILUMQxumnGyaxrs8sgNbwIAFdPVkdGBqOsEs3aQZKUCjzdRgrxHUVO7reYK8c3gIDym4aq6DrjmyuGcrWfQ0I9ObVB+0J6Z6q9OeJAR8ZOS0QhgeFRGzfw8mwsKQHbbi8p1Qc9qvQfhQxFnA2bL4GtGMuTBhMM386SfclbE07DzDx+Qdd0KFeQL5xgnSfzIqtjTiodvkd1VIB0A7XqpW122o/oyxyQC6lTaLVLQIp7BYk7AHPiLBK5pnOsKbdIdu58Hsxx3yip+A4t3QMCquse47VerMKjZVRBjSYMej5uFgraMbTTeWmH/yl/D3zWsCPWBQwd/uKYe2hygIvMNVQMjbFRPE/rOxXfDiwLgLdgIrBrY1SycPc317RZyMia+J68TJdUh6cRzYcSjNFGRvr/gyH7KYQxPOUf0uB2rgp1ZJzhmOJhWi/p6CR+I5b+33tjHwaqSqUj4LE4PxaIIsHQ262E/4GGxIdAXXJ/JkaWJoHhSw3NOluncx8T/0MgAlq+IBQI2FXNwHqGgL8UIFr5qfHG9I0AxRvDPHyhWqy9TmH9xnvqgYujE6BqHt5h4xgQm63+zmCaCG8kfzumVI+ir8EW3EGwW1Lu5o5LLM5j3bGbi4uBzoIORlDr6/Xs/irNbk6RgYFvDGfNh8vSI3as4ZnERM+x14Ypo4oyzdaiMvQYBhmU3e5XuIrsR5fmf/1u9nTLCyybdboaiLjHfhnrGBoV3w539nKv10hxFh7mMMQecm1hNr0HdMFYp6UNaMGJm/ITRNxsf4MfCfBqFTKyrEskUjGAgNV8vVh18qlaraasHNQaxYwmuVpABURJ2sVQ/GabceWpxeRgHdJexWDwJhfkemdHniqgtro3Mmn3DoY83s7CK1J8gVWNcz5fx70yMoz9Tsenzu1PstyzTXm+N+d/8PpFHbNY1MNb4x1SMysyfZ+6Z1Uq1LLho/mqbzP68k6/3L+H8H8v0RfYee2cPji0gFeUVA0waeuXpIqrHBfrk/C3RPuyHD5qsHFZZXn69xaGtw6xr7+vSSqJ+U2t/uf4aOg9/z/1Sx+G4s6IM9oKJoxV/kSpZc3rRI1cPmmUzVARsiALTNlylnVtPeLv7R1EZZV27tCw2BfMUZa2Eqap+l05f2Ep7/lHA4HibdDl38Fmc7aDJANmmdrA9QAptbJlPttUz8EfJaxCydpgTr3NurC5ZdOiU1xFhFzVEv69hJzU5IAvsqyDNbqQQsmPvIlaw7bs0nRGcxGOegJ+HNFPqJf8Jp56PjJRBjl199FEBPhHVGMhfdDTh3jHwJI2WbsvQ5dmqGpKQIhLx0vwKRTxtt+D5HGU92XjXxLhgB8tVAZb0EkUkLO4O7+XgmPH6BPTzm8OIRa2O1nztOYjKowInrDBpPpuC0DVDx5krL1GTWBOqRxB400tRn965JBsbfnMlNJNr6ausrGqg9gv/1NS4rqN26pLygkL9ikycPDEIjn2HJLcxXUNG1GnuiNizPYhGt+ZpXznfdkR06L/PBDgxm+2gOxXMKiKeR11/UXGjH+tTsbtrNEFOCbvD0TAgJye7l5oaASyhNmaUmc8XPJuVy2q+2pBOUNdYtXO6QQnoi6mgatauS2Urdu8M5RyPVxf6+EHLAB7r0rnqlkowCMr3oW2ln/Mx0y6lVNG7HrgfP50mAD8laYcUXCVTSbuh2yGbWTDqVSDAzRZ7BNDYeZM5TE16m/zsNTy3P3AfJeGVAbGAMmPwCnpq8X+kzKpB6hrAR/1vXUy8CG+HinHEu5lDlyBVjtQvaHOlPTFX8UiYPbQ3Tv4XyaPVtFVuQU6oZBqj/DRIg4MOxlDK6xUZmDjOZkNeEGhVL7MAtC8nOaLLWpSb4iRdzQ9RUqkmTDkVP6TQrpI8HupW9Mt52ZqeTZYvEOqRdI38KElXjLTG64VSlYMXbRJ+CbHw8xsyV3POzlDP1qQdRSzxJd9CrCVF1stDBoApcc4psCaFdWOBY6DxQco7CjQDYYfiC9zCk12TegWffselJH7+yov0oKnJXMgldtSvrzaxWl7BmOYJ6JQLnOVWcwkRlNrpdeKzp6TeZU10+44gj8cfNUmRUetmWqugIDh7nEhvG88sMKcQciYZIhFAc7w1RuMY8PlGbQIoEzp7GlOBTur49OTBiM3ND2UD2zrPGSzLUHlz3WwiGvnMSq0TzZT6qRg/FhwOQVFXxowt38jiYrL+aP1/Yrv+roZjdekhlS7XD1aJxdbfurtMFLG27PdFvN0xODj8kslbj4PgDH9ebB/g90wmGiHisXH9PdHJLBGMcTrfZvS1OkZt/QbgDgB7gZs+3RMUIcpa/A8rqAwzKQCnkJ+vZqJqYj/GTXVPfEkIg4L/yhplQ7lcxQmVjSk/CDeZQLuSMnOSstRVIdxZr6NC8snDHBsmFHQbY50GyaJ9HaTHrxDH940FpFhHJrlBmJFMSDb025OOUZYuGapbAXGYkkiNlJ0wurZAN3cdevIicYf86eHGqOTIun6tgwuJBUWC9cw1I00CWRTGuNhMZpSxqcDaRkyhZHwVcvjiedk/4d/UyVUpYjBE1QkqlVuuWZ3G0O9gwTlpr7MZXaz+cb5dXawUqD+F7gkZOlJOi5tSeC2WoKN+rs+vjhWQVUVD53G/To0Ly7qAJpP214zHXzw39jV3V1BrqWkD8O3MZY8HszozcGhwSwKxVFhyz9lifQ/UZQC9aLD45BzmZhN+Kq/4fFM+9UlY3wHSjTuqjsg+u2BxUtZeLSMtX/QQ1S9d0DZGKB6/uC1tYya2uXkjq0SF+2kYyOuCeBnGEffo9YDizWr0zjX8Z0x5xtdW1nQkEv/VWMu/wmsYg85XypQ9VDWb0TaRB8w1uSuBmt49jVGCj2tmRhYgofFglU8jLDflvd0XbOWzBQa9J5JllbLurQC19wRWauAgIQYqorQT4aueGQoS21qSWrA94U33Lk79W611/lILs51vNv9tHVrwazkkd/N7z0F0poYo4aCqJZgsiciLM4NyafQoqilvQeByOrSwr0URHR6F6p4O2hdxm1KzoQ34Q9DqviR7p6Zmhrxcs/LmI0IMvWz8vTj1lQq4v0Kan9zNlitg++un5LI9t0AcefWVIoZ/3yKl1OmAP8Ig6QKpqHIOUCjy3ExzNC+u7tW/2ZDLg48E5VE5GuiyHY92OziU3VG3O49bSkyfjhJ2V54LvdufgkXBL8TQ/Or/MN2SuECcOIqHOghgahPoW+elMRWiYW0ypTnZp7q4gO6gACDzCJoUUDK9bVzBNDCoMLISodZGV3DGx6iwpri0VO+jGMiYZbcVYJkgP+QAAoACQAAACAFBv+TgICAgICAgICAgICAgICAgICA/5AACgAKAAAGUwUG/5OAgIDYar+ftp/TFq19ZgDaUJvxjYRKDsNTKdr5sL8LuCrZ5ZWpF7Ewj1dTk3OhLLNKD3iTXEGeCmx8TUVRqNBN/e7LOD9IY5UCSzq28001hFYGYyamUGpZUjcUrfv5PvLD6uCO2PfOD2eZBJ2y+raLEG0wEpPt8Md9Xy4OM1OEqCLwmvuMW7Bp70oihIT4T9udUQ5AuI1MNcyTz4+cbtDphYrzjfdaJdHMP2R/gIDTyrxiIwjnRDl81GiIHXiBmqwSk97XvVVi1wAZI6vetU7B0ik38QGqlePRPhbgWhXboa+wi2onwt+Wrvqj43m6CkC7WIEWdQJVONZVosmKIlvoZo6EGKAbtGRbdLhWb4KXYuFqsuofR90ai4XInR2/pueA/F0iwdcZozkRrx3OxhMo6aSiescfvRK8BKGKG6sMiTM+0kd/xAoVvyuEZkFAMEbTIN3Ok+GgaHECz1a+FEEGX7b1CD00/uT8aktxp47NA6kjEPK1vtS42M14Uu4A/Bo1BO/Spk62wkOopwkajJednO9Msmc2RBiOZIGlWoOHXBmAz8sjThj6gICVvnoAmckL4LnWJaUAlpBOARQQgor+J897ZgPALrNxbrAEa/BxF7jLjoKkPy/uXk+N/T5WjX8Am9c1eNZObe0HHqM/WI+wXTLuoM4o0fi6dxWkiidqPc4fBttkczPrLhaj9CzP2q7q/PFW4hSJnVBfZtw7L+JrfSTkY+q4/oQtCYdkwt2DHKto9+khPuSvkwr1mf7OoF+DxDM5Y+HZmu//QRJX4C6O5OIeZ5nIQZglhUAOZlY38xTXdixgwRWzHWJUwewwjEwQXx6gOrSzq0/l8MKouGKxqqhQ0vh/869W7mJrHFaOQ6d9BdKCXT0Wqxu78IG6o3N/bdUOjPO4+2hojiylg8H5C/V3d4r1/TKCoMPekTbns7V8vw0WPxqZF8Ffm/YGbCEMvhMHrauwChry0LhxgvTIOknB6L1/EDeOn4a8VqDZb3tSHbJ6HzlFWrjbA4tl4hEeD32Fdpi6hzYoDWSUqChkttKP3IlT/unI0i8xCiW44J5Pt6Vvh7Yq9W2IkxsMawX98W9ThfzDZdVReTAK+E363xmP81em96sODgBHkiGDzdnPsUmbE72YkncwNSpsREwLpvQWvvWg4lzZdA4CqUhfkFhWTqejq/sLG8q8ECmfUYfdG3pSVCA79b9IYBy6ofYMtGa0y3SF7UiJgsKxAMkU2Ka1N4CAmWRKrezWo66xKp3fMVoiB6kHIAYKrdN1/tqQRjU2Y9qzOnvuq8uTNQeunZdOXCdJld6QjM9Rk6WbPJA1+cOTjNjpaLva45TOzPMzYZP+MBLCjgRrWeRRVWwNCjMmNfEGq02hf4HVPvQQMZjGGdc4d6KS6vr5cSD99VkGAlqyCBlg/p4Zze41zlTOGJbEVJv4cPk64QdnbiRBwHRHJTfQugJuk4yFll7ACIZH+sNLMzqpaftwyaK6qIBeodYTNCWmYin94K747LkB0Ojui+4hMiGZLZPisnNM2Yc/O53ydQ6A7zcd38b6t8JatUgpilS4SLMoDMZpNPDv8krKMw5OIZWS0SgCPEOvcSKky2/IsXZT28B54R/hdWpcJQXv2H6N+LqcpjFhkFXpmflytnHpUF+HoBIgi6uEM39h+p90835eHdobkS8kwWQjbNLP2W9dJLt8WBLpG0EKie5WnXSMC6E5mT1yBEdCBASRxLsa3sA/gEt5Nfe+1kvcSlOCeYf4hE69DPxtUHQgEgZwt5a8op5S9iZUDZAm54efMsvF7qNkPXwbgnGXfIrJyJoFMGIvXYyp5wqzTvrL84CeRM8FJHZACQG118IrBVEPzKmkJOGd8LOe42BiJA9c62YDruMlHjr4b8fARpd7z9/tFWl3GffLPJTl6zLLlySV1rCHBRkExDPJAnkVt7GvnzjVhVwz30W7LQnJdgpGKFvsiwPgZBr/YVeL3mzKjw0Kw4tRm0s6FxY+s9cbzqn5wnhZcK9b7KXtq9XK22Vr6MHbabdpTtz+asPOZY5ETSuHl7/t6x4EdCU11/vDeNRS/tiAgIBT1QADFgDVEC9Ao0Prgu0iWXgS7VFuR+NtEWjZED/BsLjbuPvV2B8MANHQUqZmJYD/kAAKAAsAABNGBQb/k4CAgOELzaf4LtLlldeUg7XJ13lrQfLP5qDyqABSKlTLIw8AXVNR8U/2UtBNthUnLFeLlIXORnUPhxPyPNZ25FtM1E0y2vMhb96Bx+JJLs0oWLKKDbCFNsgbM9/f1HJs/K6SMEQjT2dRCMd0P1bY6mWe1lBowuliVkGVRhStuWkedf9fHqTVuux7Z06E/jD3m7wki/hc/rNCRm8UWx26GjwHIZnxz+xMSpr++9wJa3jrxSeIQnQFqd6JXVCpTioQ0gvHm2DDAQEGJ8CQushjSH46q73Hy2aIddfFy5o57WAu5fxcr4X5ZnjS9XgaPgT9kkrvXGnD8FQm8sKAFJpn/fhLy2cRipSrt9ifIdYlHkyVGuGK0WcgCFRHIkOHNigWqBObRt7C60GjxlXbaHC8YwgEplrgRkX2UDOgApFaSR7/SrxNx+2Exv0YO+weFhlEC8OlvlkPTXCAgPut+6U93GtrbXXjdHXXsutdP7pZ4ji9eNm90Z5U5YV5bOXKz7RttNNU+JCDo3S7NBT8gcxZcgx8xDpAsHBskt/Sxzwkmj/7vNspUe0ky5n1+GalOzfjsltYyoEmnaC+U/5Y6ADh8UFYqBwRBkAC2BPgK2ab8A1gYJ/79RZ27VO7EA7B0EfJSycB6IKCZhkdFiZvmvSQ3pnl1ahh7+Q/YsbbYV+zrOKd8A3qNiMWBIMKhAL1sy0G4k+IDQqlAQvI9xdb0l/gyA0jHC3e1RoRLuiu8FYksyIpMQTVg3SYV8yC/VQy3KwwtMNUoZ7n8c0HNyTQrfF+4o9XdsOlxkECWWE+MoAzIbmdVtQ1AuwC8yFRjXNNx2tOEOZDmPMALVMLkNj0zLgCgv7UuYJ2+tRmZ/kquvppAYxQ3XGpcOgoUjiiiNxd5Tkb0i9R0DnCAwX5Su7alWkv7KLHiBJyxgguec+jBD+jtFzNO9EpeHMtZcSBTmKKPfRAtBqChdVxV3VSsw/OAvmGTa93ggHMogulOskIvx+0QCR3/ylLjXw9448lwbNFZWKgDWFshCm3XdDJRaGmvrCp4l6CgGXWeb3EXBoQMm5CPpVfyYdT7HOoJ719F5YjEsLHFfliFd9d80WdDAMprMutlyu3xP9D61nOcqML76EYFZKxpuViUletlzi5ZdRInrap9X/cuefVoVt+o6SN6z8KbaOZC14xFAecdfJD6wl91y6qNFfqiEMWbISPVgz0Fo1Ri3/OVzUzDrAkbDtVZsvo4cScIxHaadR6jxp9qO5/KtSYOws+H+fXyayEd8sXBwsidJv/OSz+1A0Srxs+pAh2wx9/0U/sXjX7uPgo9cPOfZpenTU+EDrJBHHarTUQmcBNa4DF+04ytrhi9ntytHqUOs2Z5IdnkAzxGf9SDBGLK03K9YiQAz4IjXYe1CF+oE6IRt0++vxqopsibyMe5DnotSyPGaoPQU3Jqv1fs01oxk/vEUsyZdNlxoRsGMYYRtEoO0/9DF9U2cPMXcG7hiHZdXYcQkHArwobXQhPGEI32/hTUDMBWE/5tIqj1EHWRhIAaAN5ko9iB2J3g0pl7zEudwHiZEGwTeuJHQmeLx4MOKQLPECIJ+iZt7WW2fryjCQj8CCo3TwPtgrGtSBy39j43CTTwPjVyonjJx7wxJTijJyA8bJU934beFEnT6FCuDMjdJuYasPxHRQD8v9xh9NML4/suhQ27MF/2303yOQnr6d+ewGGhKCDaU5/oeYVo0E6infYAkHAkgDg/uLJK0HSBMRtMnmFxfbyt7bYmS0TmST3WFGeHCUCgNTTPmAibVEmK7w9TSVmeISgwDF5nPXUAY6dvK2j7IY3JzO7TLJUQcZNNaRh3ru0G7C9+lbhPXO5Me+tzIvUdNzDxVJWd29PfWF1X0tKhF3J9MV/yQjaCZQ/2Xs7aELVZwLE83VXQ/yHJDPPLiOy93JCaYpw1P1It7UUF5SvNX4Y8VO2OCoEFfM/G9KR4VWhZbc8o1n97oFlpBZ7artF+bugNzhGAInVTaIcfexFLYSEz64nYFOPw7VAgIDajX2eWWtlSNUqda4160JILSK01VWtdB8r2l62/Afiq4q1PJ4qbUZ50y5pDqnNbcuATXX/YpH2qoL9U1RZvhE6f7lIct/8AQ2tiLLsEuGrsbpKRCdjH8ZiBeml+mX9i8MLmsY8EBGaCwMK0nU2XiEOXRkty7IKmYpGMH97OcnNwdbzs1MCQolsDyrn58Ozb7Jk++PKEFTeHhy+Gz7OP9Yooa8lwuMxx77GU/Z46vH7N34MqKhc8IHOqvsWSSr7LyEan8WC8lT595vZ/zHl1BYd9lnwHem2Qt9yAX6lEaKWXp05d2N/UpPbKNMXnsr+DVMr7hChlv9zvOrjz3VhWvMGIRxfyO8f9/SziW8rQ57dTO91HIs/MdhQpQnRnGucBeWzP3BMjhXr3Pupl7pzCdOsxZD+dztTwByYEQOg0ZemSFsN0OgrnXVvQf00IybnOn1o+Db+2cYrdAKNQybNEOqy7C3VuSR6o3s4WPUUt0imOBjttH+X3cTIa2iSbBcPxf4AGmWBhycVL85atv80vHWzMCMiD5IG5oVvEOXJkgwoyv9o2f9J0Opiw8c96xwzf5x1OaT8LDUT07y4ix3v07URA+rAViHCggisGyoQc3CceVpkzywKmKqmA0DKhm9srhwNEvn7FZfAxXpiID0o5E+q4gp3mzo7zQvZZhyRn2FXn7+6yYmG3GqqyfZn5kAeTaRMBDjoF5MQbN1i6qXDKjj5xVaEHJW6DZkaqK8Nh1vngMOepeBaGXuXU9qYpQ3XKEk5qHAcMrxq1DQtCyShvRnjA5EliZMkn3K7MBkIT/ZE1ZtUGEpTYUGFWzu9TMP5NiWR51NUcd3PW+WYRnqqlgF0fHy/WtQlh1reYqEN1/60Q7XnlL7QHmrEWfHhHaUhZlhw6mWM3/CugUDp6M8Tr4WEsNDCX0vSU0uEwPmRi/uGlpq3w16rdtpeF6ZdMCo0dj79qVygw9dYoPCXuIvsTM9OQ60X63TsAhFnS8f+UkuEbBopUsL5ydb12ADpYpDTYiR/sFXhF9gnolQt/SoRTGzz/bJ68ZfUIJt/JRoC0+i4+Dq5mQy345HNttCs9vuYXidPBB7376i33jA/aVgaPUYkrKqaNP7eIh2ip2+fJM2P8uGsP9fAUxtgZ5x7qphKidDbtO71V3Yi9ARd4iHx3WJAqHwnmoTmZv8u8XI/lNmiY1lbrRj6L1cfp8ggzUEfAxl55NYa1W2AlLmybj7ik3UB8UMK0YWYMeQs3GMhSISwuvWKEvICdqXrPhHlbBAD9uFASDagOXErYAXqwD931z+ANph4FnDWlXUjgvxV6sG/2wr9i2Bw702lfgUfFczkMifn9KDte7zQ4+gHlBQQcx3u0OA8TeEK+22FtX0i7dDiUSvPv+4m7Nx8fpxbqKcStSXevxEOoOndENet0/8UWTkF9nJ2wmA7VNW6YtlcGJN+Q6rNnlEDKayJWhbNoJoSHJpEskCvuQOVIfYoVQ3f9JiKyKFoRYHn9+sjxncZxktFdmYZDQG1Rp1MvxlLxbdjypshtucP6yYbVZQBsTVddP7eUyY/tTsfxxSrwmp5NZXWIw2pB7ImDoSCkOTH3r7fYKwq6Ji+kV3Ro7p4Uc+rYp0BRABfYKsyy5qXDKBlRW+vEBKdHX4+RcZxjMmErpKjgL9zYhnY2VVUhAkDXpzDHbWXJi3qlEC1slgG7T1aBKAbc+jFh0wXioIJ3lDCW96WkX8SCIBFb1J6msoi8OLwDqM1muBzb8/H0mIMt/wTKbuO5u9IbYl+lADCiBrS8YsUrdW8OJ6egdZ9jgYn+CvkuD8HLEpUKzKhBf6AgOFWOiTwpiGGMJd2VhvdEg5BcLJVjsWYt1M1TITlFjuKXKGMqoYvIrUmIgrRAI082EkyTJrtNcAUfn4kv0RH/Bnwv1/cZMith4j99rAtPAruVO8u7dURMcz/IYc6tRnogEY2Y/8srWB3DVRwvDIzKmIZ/FE31I77VS0jYNYNxFFbWJK6iTvWYO3vQZFMlahe2rtujGJx1fV/0dBjdXJ/0XR7/uVtcoZmJIx/FOe27uLJlqS5KOqIheJEBz+b4TiWneFawzQlHjjnY8/eId9JzUS06FwcTryDcPHib5OwBq1g705t66ByHJ1BidHip3v4zDeSxmH6FFSD+4isCzoWT8yKsNaVBVNKypHHVDCdODVlmXGNvD2apixGjciASa2rEJP4f+i+GBAJlgBFMcAkL/ifDHlSNJ+X4eSuW7BvzxS3njl0CEvOrRC/8HWcFWxirbPypv08EkXRgps7aiiNiv3zP+ur6zfE8PL5Ex5zy1ktCDaLcsC1C/xbFU/mOtdu+yQzjeEYOSz/Hx+oFIioeSsa474paO4kKdRsmjUcf4RjP2748szO3cq8GIEoVLVvqalC3L16xkV4Hu19Dbn7bIcTpL3kQ1xFQHvX+z6obRGN8VKS5PArQbWYWcyV4wUeNsOudvtxdvG18fBuQiyZoUOkJMexueAw+mGwzJUwfqhuTzdtdT6bXx1ZSdSvuECWvgt/A4jDfSO6iko3jF1egxvp2jWeJ1lQECdYtB/V9Eiwnrqx7hpJmthpYjNcVtugZYu0tH+svKhRXz+knB3sYaV21S9Vp/1YT+dYxWj56P4PCj5R5syn3aBAozvGK5JWY6ISfjeckGaMd4C5Y1E9FEXXSLrcVfcqIO/WwwF+AXRjxQaqnzLDa5qQe1X6jNMp7cOBiXeVINsw3wqzJvqDGfOxBX01hfcypIPGDt/ifuzY8Qi9QXaAtId9VB/gEXRZ11hB+iKrrsbFbnyjx+R7a2fUPDTc8uLohMTTAVcp5FhC/rbcKCqrLNei7rlJDLXOFl1CdUc3CfEhD52G4f4pixJKs2iUbE72XixdJoY0YF/wT+tDwlCn2me0X81Fu9NunlobtLOtnewxOAEGhgDXAdx9A2q7SelcVJwLdFeqBRk3ictBoZEZ30/ycaoOrJp4x283oTn0UUb7ja2hdAUwlxybOc8w4PrNqp7K7m8o+gkRJE+Zt6lPvfMWtxrQwj69zTYnzRvjvzCkAWmkzi2DqDCwgcSYFyUP2TJW6CSDtMiSam4Zal8bZ3SFyOVncN9QQTEy+BkSww2ZZuKeZx9/ucHzHKoikICpaBMK+G3JmHRy+aeL08nqKrKshGTYIEQfIb2btPTiQN77oivrstp5hxsai1Na7MshxXI2p0owuRxqXxpfJ4b8qgYDxlCCxiWFlDxXKtuJRhjEx7T0+L7CNzfNhT96vvTwP4DrqxkzIqG0wnb1c/HVhx2xVcb+DffTnhZr5meiTGXniWSOlPlVXL2iV5ULY5awhu5/+DOTdmWp1nmdTDAmpJy1Z3UP20RnV8WVVXwXI/e4Tjb6jOUTR+0SpcaXtYgRF3lL8UM36JoqzOJQS2vqr4CVvOaJAHMW4J93JQMKV7vctezikGTaTvaWBoCAlrxaItBCO3X0xZWtKqEtZFFkOJpdmrFbYRpLoUSpAPV9uHkqoMcTBM41j8X/O/ZEiKbhU3B+tQhkzIBRf+asyEWvUoDAFv0xacaPf9a9GgGsrJU+82tFSo15UnmhtrMVj0Mf+gH/NaQvccg0c+rXRODDJtLF1NhblPj9KCA4xlswlitBFe021qujG76CHtpOhnyD6TAeIhv9FkNwMOU5CUojxyjPq+LEHuH0CE12EXlEN0tZITuR4IzvqaGb78SfTNKofQwFYQhzNoMAO9PeE8FVz4jrGEPp98RZjVvBc3SrKyuKmvwyuj8Rr2dlwhC7lv8dnRvYjBAd9BG3LM8ljdqGt5obi9boTHh7raoR7PPFWXmaKlo4NJPfNxiNcmkmk0RrkiqmG8tuI7CFgw3rp+lGzewh1TKHhRvJzNk5xBoWSnzuaqN77JEqO2GNiU7ikJarAYOdyMI2KguHsa/sFUWHad/fFB5JAqHJDaHPRIqDW9bi3B6oE1/llEXbbnh3kREHusUovVBKTt+CLIcGc11VGxtZ7waWFMJvuk6+IvuTiZerfHHYOwHyF+r+GXQ/0cN4aFpnhesbdf95MyzqRqr4P0YvhRy7kTtqjIB9qAqKzuluqDUpE9s8WwwEajv0mwlPcqkiC5PxdKSWQiW8K/IgaBVXYvLgoaVqyYW944mcYoB9l4RHtI0BCf38bOQObP98cLwN1Wz5Mx0a6TbluSqFoLZoLMUmNMdPoJnBlQf8wG0GSlGOWgfeH5hZ9Cy9u/ahZNfXRIZbVKuJIVo7nto3YiDB4ZRU+acDbHxdz/k1dyFHheO5n8WrcPM/d1/3aWoSZ56Rf1NRG6N9DU1OWs1HFMPpQXaQCcIvg3SanKTovuoXPasammdQSVLTUPUTdDbHe5GsE4dmkjMQtrmJPWsaV3fpoolPtoPRQP1gCmVyfFFnJd24sarZempyzHZNT6YZ+kucgu4dI5jPjatZZg6uydYPgKe6GAVaXZiF/ftcpkuSg5sUFCMizAd1AABZMawdxXTaI7ThraXsmb3PeBEa56FBF1wsyv9JXni3+3o5I9LimI+agP+QAAoADAAAEhwFBv+TgICA1C7Ti1pPkckLyovNe7mntZPwNmQ9AiiGZg65DV0nS9vf4Z4VIOJDDUEW+3CztkCZFReqSxSr0Yp/TcAFFld1RLOcuUqsUySOimTUvsx3WF8MjHzKUx1LmCe1nqe64CgTPmzyK9d/PVuPg/C0wMfYQgw6lLyNFgAZl75Rael6DwuIy2HOvi0RpuAbylptQG4l0LK95M6adwqaXfEtUdsBqbUnc+JvYlobT4j4c/vXdYTKUGbDk+uquheRqCUByLp2ZbwUJQWSisU9QLAJ9XlckREM/ynLn35DtnyMY50TSG3iptkUo23NQ/M8IATBeSRyqd/zQSvAYA7sYQqAgJjT2zOE7ttDW29sPKHNe90ytPaSpNo+8UvdE3qXt57QVB91uunrsVboN0KfyOVOBJQUfz/o8O96Wd4VnWrl4qJTvBIcP4G7JZ85RIrpUSIFa9DOgVkmF0zFWHj+u8iDscBM88IwbF/+qphoiO6iBhFtuHKjw+hqkO7Qog0Iw0D8cMsRgdHe6NY+xXHAWxUzorUriNGd9f5Th8WjiNcYb2qIagpFpyYnrKFlHTfDAYYGW8a+s+lam43OS545ADcEWjMyRXmNLZN4zEr5cu5xVdZfgCd0gYXx72gDs+rpZPsPWzKDp5Rb6kTOdietYDFU+OTxLeV/Gs9H4f5dQ1/k52cP935DsfO+6bleaR+lbTTm3rSHJDfzBzhYJJfV7dFLWIwW9F3Ro4q7aO9pGizUTWv+A3Zi2Fr/X4MN12pM49khtEQEi0Bcimp/oRjkUHub4as/87zVEfnV0YOPVJASXHQQM8uYHFDt+0AgsCHbfynCn3xn9zT4dMUqHTTUdDh+bUcgMPILBz7mv1MyddMNwCZ1P1DInaD6ystcg9cKIk8T4p7eUsh9WxTpMzGOB/9fopeKa6g7gVfzr9g5Tw+G5FHFVIPUb6sD6NOxiTN0bC4eLqKK5+OPBFbe6yTkDup9NyEPB/CxKWblDDd97KsgRtTTxI0l/g84QKLTEsQ+Cz4L8LU/DF+1I8I0zXAzEjwxVWBpC9YM9fKe2tOc/HlRmvDK+f3Fn8lHBx1IWPWS3iK9QTlByP8TVtzFw7Evg2VcxSBD4ubqypi31BLdYSLfj20UK8KlMqhmMpvn0BE7eBvwTQTBU0LOqnw/EVdwmhTNwew8DXpc3p5XaLu8WUCzjT/zfHn2lBgTQ5+vAbODDh/jGM9XSxyBFY9U4PO6S2SIlSDYzssWFCpiA3oykxfz407X7+/+qdf/ImDpQ+L2VfELzWWinISPASjcNhjB4euo/qmao3XuCYyNkEZT3f3ZVx15HB25JG7Z6MHN4BaD4vLhZizSoknDG6AlvIh++nPj0GsT9V0Jr0lONMVbRvfzesQQn9+AZDbBbYKmMD+0DIXFqg9zpdLHma/TD19WdpLBEQScXLEv3P85YIiMszO2uoiVxYWivjl/CJCRpSkzx2FIXOk1wnQ8X8IkHCQbN+1Ub4DwpLWoDwBN5m/ItD2GmMw2IqzCLfYiM/CGa/keEPrywpbiHc6ZU4hr9JIECBhYTAdGMHq8tBDJCLtyICI1NFAapm8NQK/IvrDzKHEShSYE3JRPuXeUuOWxrvayf9ax6ZOxWE3CaWB+eysCpjSop5dYlPxF2W2bJMd5mkN4AvUZja0epnjMLUvkrObwDm374iR5Ye8sMA7K2LqhSEjdCL+V8xAv3pmqaYfb9sLOhNsY09qNB3K0g/QDNuJp/VVnFzio2VcE6E0eLxHOMUIHyAa1+VCY5w4E2q1X1SMI3eqKWKbRjPcXlx4Ki5Qb+xgSjrNW2FDv6E2c7zYxE8+dyzrKNfRtgTNG06VGbbiCE0dsS8bplPfNChfaGgTuaSeDB8Y9d8xdbDxRPiemeaWPyn/5LBP7H2ReiC86UuGhM0YvRdWNvSDYmwwusvq+2lZnhd4Jz7yY3pisrwT8FNweyYCAlX1ZbMj7T7e49Wrrrmkzi0Co+dqjVjmhuXsZ5C3QKouaW1CQ5AiSrtTHuaA2G54gV21RqBvPRBCHRY1Z3O0vvFraP3VxlLlZwpACqDo9RDobcCnjtGE1TD2NSZ6rnHRmxou+ZxLnPpPVTHjPoyZC0b6TjbdDEImth5cburWnZF9zHzFUIW/6nQ8X4ckORZ6gGgCjWn7A+XaeBw6k95K0tzuHB5tYhUv9d515K0RXt9RNHtaJEYauIButAmKnDCvexYyh7d2QyEoO56UsvjCjlwWtcdbyC+12fknByTjduqiDXXdJ2H6Id6NGLAgfD2zTCHEfCdwlfDrxYw5VTIAJUV7/dhu5rtfouLCaGsk081kea01Urjde++GYikM52zdKUdHcQpgayDQ3gG4VAt6ad30YIQ6RrDdP2NdXRaIJc3xaJvsv31fX8EckP6h/SGUf1qKd4DFd4wTtXEJkoqah+5Yx/BiOeWLnCumVLOe7jGppnLRMP4iuHDx5Ahb9CrNF3nEpCf7BMQP1VUy/OowR4gWpgTbw3F+BoIFJJOwkwD4MLSGaAIjmyFmbeSnc7cEjDqrR8LCytz4VGVU8WvQfYXCrgCWhmEa5fSzuaBUXCXPVjnp58ftEnqb1/x7mAr3tdATcX4DqYbJIyDlqSFMNMDhmmN6lloyhPcsHK9JAlBe1+x1iVXRJMfruUcQ5AU/WzZVvYsdnObgGYSB+fMgY4ydwUG8Zsn8pU1iSzGxljWT4C4lpOcXJj6lM6h0munKHmVFdx5L13dxhW3a07AwH2GW+OHYti6s3qi6OU7/4CB6isyrqnscK/qk0a+qJ1KuQfTYQuvu4qfxLHTsgQaNmcDGhvp3zkHq2K3UbJ+aAy4xF8AEYA1sZdtgbaUVn8nOm4c1SxH12+jHCWqiKZWSQhU7QxGGTvQSEEVnSvs/uGb1tvj/NAQqu2XdO28Bxw8hUXTjCfQO22krHktO/8URKIIOmTtb4b1+AgxoHfTvbKhGS/xP9mukroz/UoaRXnn4ycywcq8fGqgeOvNTHyoXjiLZMo5TOuQxQE541JUi8n4n45y6WNWJwWgG0mKMNc1SdybybwGa5t9CCtZZoIQ5rBbeu4J0gx5/PVHz+xE4IXfXRcJVfcdCcIBsJ8+wriJc6OVFzc59uz7l1wU8EZgwt90yDyQ4sA2w+q+sTBNJPsWfr2gVkLltTYE66FsZCYdDDli/RoeMRLqsuCBzvSAY86ubLRjvVsDXESKbKB+qCX6s/5lQ+a9xUSnNFXRHj+iZQ0+92BExnOqHU7GI56D7JEwjkGbDMBnX5d5IEjJ2c6ywx0lkKjmpHuhE6IIkUpmhDsMsU/DBZcvXoaRlbQWpwEOV6YxIvNm0Cv4NfsEjLTGyycXDNgay9lOOBdffW+08LObelMk+DUmC5yChH0s+CHZe02iXlTBmcDVD+tRyI2kKAtkrwAFQendEPADfw9dlo5dgtYirLzNmGwDjHVKbL1PvjMyvPUPsiXi3G9sJTzBJYwvMgvoHoP5k4B2agxzCg4Gq6Do/tfTi7GMxVE563W2SYHFGmFmYJczYndZcQVyJCAiLVz1uFxfO+ivFTcTFeum1+MelGzoZmvwBS+IVAFtTbHzLggo+AU4JWDoRylS/HZ9Lu4NwIsRfObb3QtStClYlifkRStRWWaTCy+Lbp/zCmha8kPcKwXbuFB/YSkNVaYNBnRLPkC9AGdN7AU41Fm68B1hFeBnCHfb76kKmZW3AfhWQVHJ2HMogRmBhi+HrNYAkLsoDNRBEo3Dh5JJ6O/1a7rQvtIPuEw0WikENLxomjvACbmLvwVnPeOiN8hIkNhY5kusB3D/D921faIGzeNLVMEgm7mveuSDPOL20dWsksQIwCaAL2zVZpftu+gDFrXUjsEuYrtnBzELp3geqaIb0rhmLXWYBPzijTi4a76IvY+Qd1s10eSPzECPYVIJsqIwzj6NX1vutplUXs23+y/11Edk6aoMOjvtL4Rg/uVSOIpED40HRnr2XnyHPiGMfp49SAmS8RjwhoPnDIp2obE2EWGcGrXogwjxNWWHUEjIxIyKCasCA7VKknvMV8f3kbQiXi6HiaXz/cVkZOmCKrLxrTH77uv2oPdHGgmKCqHqcLFgrCRxr7gqT2Bfb1aM8LJqAC8RBGcPkWCCyTyc+p6Y7+SDQKgSgadOrSzDfduZ3hQTkdLJUItQuFsjv1Qp4PFJLhEs6uj2HyfZz+6kuuPgiFJ5dvLV+OJA+fdmxL/r6oM/R6Mgc3jq5J82JolNE5Vl/TnlzmSLZCMvLvoRDTNxbFNSCvya3Q0pFADb3IsDqHisMZas+OgsGkjFUReURTa+pE3jjcSaHry7pVb2+OTs1NYkRrUIQ+oKGJVJWfHIFEaDroNYYZxosY25UB6beP1Rz1FZ7f6x0AV/+A65F2ULat93hi1nrcvoo+8ELvyXlAfQDkmOUytgC9eL4+KLD2JSYqwWjBNwvsca7TQZCNVipA7RiEkrWsOJ4imHHd73dKbdAhLxYr55hPcIhtbg3QeHn2/JOyliDfko+CdSX3+7lKxi+emwAfP7B+O/jdlHO5t1uOFH0PEjPuEimQpMmjq70FPimDFdvr+vXcRaFkSQeN9LPpqQ/q4Jo1YGHg3GciEGnGfXI/95AxukdWIO83+iFSfRZxi42x9V1EfwhHMLOBbLOukErzCIIo3LTC/Fv/XCOd0QQFV/81XjLcN8imK23y/mPZm/H6YKND94JxYJiXv3TNVuSs0ID4Q4ds13x0g6MyuOf1UXc0/bWWkWyVz31mOOhJZUfZFPMz5gq4rCaxj+vNa+743hak5zGY/mOwmOCqa1AgZuurGdL1MD4RhbF9Ti5Ziv1vnBSSht8T55exS4rkI8HRY4l2u9r+aZO4/NBNrZ59GYP08m3E0/bBYmE/+wBnW9wPC3koLpH5Y5DZ8y2/9N4rYEWs2HdsheeAN3ff9/7BUekTZAg5fsopnH/OgcX+VPN7pADF8ocpJrQ7TAHiKgsSxQf/WE8k8W44+ksb1xVMuCwi8Svbp1JtEudk6hzKBImjkx/RZCQiwEK55ftD9VX1oFQp9h5bld7UROjMEahwOHTUVfFge5K95SLO7WINV1JEFem3e7tX9yxwxttUKa75B+v8MAW61d3wDP6MH7d604bOE8tIwxcKpe74WnswYV0tVAjHU5kCVCNSOcJvOk+CPepTdQDJ4wxX0lKCJTZngXoXWgArvYhJZtVviRgUPIou9ADn5IpIv1iYD7jtZt3MgFgKIy04KXbogNVM5E1hPmEN8FXI4twgkEnSlI/fHll38w5M52MegPQTm4gM+5feBX63Y9wQHTtoifYb1L5oxoCBF21cx1VVehCFRQwAJAere6gJEB3R9hBGerAMWSV+r5G5QIDLcM4CaspdmnC9omfLSEnk7YqJx54XAAwN1TNM1veAtmp/UMyF3jqPVhwxvVatzV42Mdw2SMrYTV8Z4PLs+0ZUuB4GnoAsUMGIobiu0moHEoIZRrxTuSmYfG3KmAAcOk2Jf8w17wBFeFpzNTxaMKyKw3BL19nO0spOYk1tN2AS45lYCxg7W6dGDQaRpFhKK500QQVKz8FbQ2iSMq9FndSeeVLid8aLBf9Rx0c5uHM7azXMmRUjEMwHM/mm0Pj3PG13fQL02h3GCuYBRwC1LdUBk5uub7E3BpBusOC4OiqUWcr4uLuYu++runTZo9R7okynvae0J/HdOc9hUG68bQYmEghqDfqGshNHRt3sPslGnbwqt7GTVPv82weN+GBtKBgjcHZGe2+Ncw5neznva7TmNwmCqrhDGs6uri933FkwArdlxeS9ykl+Uoqtws/3Cel4FN7gUVjXOKXQ15Y421Irvzr975HpMBN5I43z2J8srX/2PjrMAogCRWGfuMJMLztm7Zqz3cR/osBxvF6EQ26d8yM5OgBQ4G7aZG0nYSl/mTeUaeswAPYYqNTmVczDUwTTlR53PBfe4SWR3JMktT62pe3pb4rtBtRAh7hnnO1Grego28d1QPtWSvstaaej4uif5S0aZ2gtLcNLTzsOBxk31ZO5hGWy948FwIDwriwys41NYWu1AQIwjWZoxdqpMPN8QcyMnsEQBHLoofDQdjx04woPbc5luX4u73ql8pZ61qCt6EHcMRhPS7/OMyg1S8WbDTDzrhRTUfVWdGGX14D/kAAKAA0AAAwrBQb/k4CAgOh+qv1F61WlWovzi5uK1wCWPTcFmh2D8hoOT5Qxg+RERObSsnoE8pDVkgiOveiyiMxHkovmNuSrXhNaDRx++/B9VCoEAoeAhpMhd9HKp+UZp0cY4tEA24y1jwZAG6xu2SJKu5GDPR0r7YRE5ZRVKjvTRqOoHSlPXDtxS9WLSwOzVK4aVP6RarpE5ynbc8RAm+NjyHb5hv1TNLh9w/WKDMirNG8GE3iriI8fXhC/7V0Q9cvFcc2m0rv382nn6Kt42NKsKScr74arbLPr3k6fSBbgiVfMVkL8FDfEKtqA44WByE8ieH7gscbeh0wU876JEkZ5kCTLDYCA3dRh5znYeUXihd1Ta7e1iqOU/zSOD6MBIg+HrcBTkYIe3+nOkcR9YwtC4zLMS5cmEepEro82Xp/BzN5JKs+ub7fB7mYt6CCIGa4LRSA/S6p6htvoepxlZqc3vLDo3apWv7lSx/ra1D7xX3F5KUHG6jCvmTfZcWVI1nv4/3p+mQKlvGnca3mXbCo3hdXjxMtErDFJ4sQvV7JYvh1La6O/GAzVBfgWWfqiDONa6u2MaZNpYUEjS7GrKPi1lPwvgixlcpH9139j7IVFGWWpaKgpEfEfYZRzhydPEqeLuGAZsJ8YuiXOR8/FJMohz+DLCYInj+0EmVZASM7ZIBZ/EwNdl8/HpqW8lZ2B+vPoNhM32amqUVTWdWJcTM13OaHtltkH746UAcUOq7s8U1OCpVjXOmg7Gh+ZFBD9ZHseUslqqS111kVOxxZ+Mf9odF6xvI1sL71KZ321tjhv07TjBbpUAomGh8frnEGaB1UXtLY1yZm+HGgSaF8ldy4qR/jBk4dnKLZOVxFDe6nvQCISkitglIol7U+Qhc61y2qgwOF5XtEedUG9VfOgHKoYWeBFozvnixqZVpy+abRWe9ZQT/O8e/DW2+29sQLbbi3RPTid2yxgjX4YMuXKYrxRvgFli29c4LTNjNw6sMz+vcqsr/pazRq8wFFovN+62rklTdjFaquJsPvU6vuH0gzzJAIjD8ATPIzZvV7dD5QPIbaKrOCjZOJEYcpcUyrgoQO3xO48uLVx8vCIiiJN1MOM4MNMnvd6Q7fcpvhEzFE5l9PPlGRbfVmv/xirKkvfA1Gi+/RY4t2yjii2hGKW0k0RzegtH9ros9vhvMTst/i8oRFZyrZMDBHWoOSlZ4zmnkBFVGRSNFW8Zt4qehfMye0B2VPjXcRgF607CgHN7IXSeO8chEoG72IG30FRRcrdCp0DW10kZ4CA6x7cd0xXesNYLT7U7YOUDXEBvNAOpReaBc1BxUuAzHXlwGsHRDJCnnd0fA0K5glmJEd8aifyQSitGGR+zGrOLdmEwE3leaxTrdjxn5+BYs3plK1r1EaPauUO3V/VlD48zqeOxzzbwJrtrFwYCWh3oqKBA/xJrVb5ekZ8rYTQI1JQu64MxxMkRQDY912Cgo/FnBhYhjsLREQorndDjBw3p1N/j/6AhmG86bRA4BIlKiQ4JhpnJ3BEfnC6KzKqACnNtkTjVMd1icJQw4H1J/9iJJjy8qbLNa2uzQjEMIJP93+rhITcGAxm6ldBJKuZnvyDoghIhQxSVGyBlmGyu648kZRpYxCeX5f9Mf4r7AzuBIQ+5QtZerZsJTq0H7NZnBniWdizwOIinOIMeAXo2WQ5l6wHz/vpmrH7pwdpV30sJ+rMPDxf6i7jBHlhjCM06agSBv8Gp0TbC8uRJYRC8ywi1epnSAlPc8XNqSxLP7/mewKGeBskhrJO3XywDG/3tPH6BKy98Quo3xbMPZYSVufMq24+MHVuML35uicP2YhxUh1UXf075o/l9IzgUVflkJYWVD0UYpprtEzLVstzzdswCE3Rghd8ExdIFeEt6ZIT3+H2W6qhiNV0Iy4Jq/cr1Q4cdydU0C39ERIe1aYfXe6nFzpZS2emrAo/voPAPLizidlhOEQt1jvrqW5PIfBSSCDNHNdtyV4iImdHBmtXqv9Sngajr+CDp+u+pES1iNdTnk4aSKPTI/8Yb9KcqkvTh4/SQBKIEw/oyLw0YqUBaY4kOgdXSJ5KyxCF4H+s4FYrrR2L1+W3iq4t/vqhNHIFRHm9MyAleawU/hLYgOipM3NyfSyS0IOlDleHcUOz9LUSri4ChdfRwNVthY7tFBjifWjQf5m9IaI9wrW1Y7O/q/9EG7OFTk3FiDlqge3D3lN31YXK+Fpm+Yl9cPXiBrcIhIeZIiRshy7Qm1q89dmbLhffEEnHwyWOEXohHtVOQ03AOSpo7RABjyTTwmxKoNV3++YKNn8764jdC2S72es5JqMH6Oo7LiUG/aXx8w5W9m5RnbS1JfhXMMs4gT9BMqlUX7NH1Xjmw8gm3oHT8rFjRov9/ooFchgfqDJYurN5OpNOKMzj1kUVotkKkl0TNTZJE/wBalm1AFBKy8Z88Wp/xbNkJU9KGbgWm3HA8sjYTunYNMGUT7DjKScB/4CgPFKxPobZvydjLT9VASLrZrDBef4uj4fxwmGTQlVqzPiCdKoAlvSAnNU7pvWNFJEbIDSCLsapSl+j+Ogqt8rA6acEiR4wwagfJVv+b1RQJums26nHdrkSFx+ORYf6bsN+cptlzEem7U8+qO97NVcJ4QG2F6G6fLFB2482o1c63bsMywohHUsDNWBhmspMMuU5otwp0f2HEY8ntAKmwjQscMbhLMU/4ipfw7ksa7CeD98jOZj6jbdLSf8Vz9eevGO1TEO6LQxxJ5bZWKnzCe0tlmGtDUb+OsPTvb/MOWL4FRTeo3Vx5BlwRXnegEZwfQ+lwiQnbLs3IqUThGuVaP0HKrC0udqQ4K4w8j2H56Q30NSnk9KK5TbmTYtrY4CAxTHGH2E2HmIkG4bQUwt1OAEH3RyuFRlY8r2b1qCsMA2nHBY9KIZZWcxTCEjl/FY7/SZjjoQDdnf6FIkvvwxjLRHZN2oYQ+b5Mq95c2gqk29wg45t+1jfmsSx+2CaNufSwCS/55HCR/IhmRtGuKcHpzqtAE5JpugypdWO1tgUFkm2F6/u+Ae+GbQ6Hlo5aBLLb5rugQ+bIznyiddreUDh1fwZD9JCMI+3iyBIAMdSU8Kfn95KN8Uhme2+uKMR+G4zLev7/2wOaWpLlwStaJgb7dKyMFA2Fxwhz3jKVpxYj+XSHPfumtqtyz6Af0r4sVQa/chB6q+H+TxCwbKHt3RDmINC9CnR5DTL6f26slPdXaY3r/qseRoImqokISCTZ+HjoYPpZPkrAQ9LqXt8ZBjBtV90FcjODRsHIy2QeJylEVsouiv8TAmIddPOc5Ll6oxpGAKiFXPd9k2TTaI4RV8eF5FpjKeOaq0gjZj/Ip2Jjwuw+S0XOE+xCwOdPBbExiNEmA53NrF1cnoSmK1qRsy4R4xlQq1h7pqLpvuhyg5nYnRwAitFlnUhkGj0IBo+jNojcQOMTvjSpTD2+AbH9u+J3/sS5mvdQzJU31nL3TB4JcXVPHjAIHnEQavOnnJ3Q7Ee95iJO4bxYHQRC8H89wjU3lZfEGnJEadUEfdLq3772RGxpmcPmW2o/ZlaQlYPwVNsDXHUBpllgtPG7JpmYyqKDtoXiyom1dDhpNva1eZLm3joibf6IVjEKAcAlXJALNJRReG9E23aSzj06kpD0mHE3VhvgICARMnAtIAlDw9cesHqy7QGUDhx/Ml/f74Cqc0xyorGo7+yWC+s7sBkYz6B/x25u9VEge12dukidBPzm1BC23FtlEvLwlEEjOus9vS1hq/PU92JM0hYGnHTMCEH02CLh0J5Fo3a638KfHsA5Z+oWHLp31eXHrfBVN7cvn/PNxRnWruFoKE40gCYkbgCoB0F3OIvEUkFblxQ6efzrn2VHgPEno9hOgCuce6aijxx/ZJX2ztdPIpe3CNuDzz/a6x6r7EEFGVUbFK6GsNHtuNdqJ4qzBM2fcr2BUZKUILEJgASVSwplLs84HuLx+In2f14MdN9b5IkxUChHmmU3yghHvelu0UuqwmasyRNMAWo57isnlxc04G7Yz7ei/4cD+Jo9G+dCx4m0ZrX4W2Va+w4ekqWbwajnxBYNXDp3QbvIKizG+9W8366f3r7SEOlbrFHZcf4vvadiXmbxhKg9DKaG+IeVHJ9D4CA/5AACgAOAAAAIAUG/5OAgICAgICAgICAgICAgICAgID/kAAKAA8AAAH2BQb/k4CAgICAgNQfdFevCiB2kIF/OUc9TD5Z+AdSfc3kPeE9TnYgQwnDVVKMIrz+HLPsqkuxEH0L26djEfy/FQ5aNHszSnZvT2P1HKAF9bNQRyNVgnlWloQlJOTzmv0n2Y0kj6JkL+QYF1rZ9IzatDxW7+IFSoJ07H7WKCpC0tYqrEk4tQMwqoCAlZLQAY0Ax2WtC+cUk7SeEoiMkqL6FQ80vI24R24CZtm4D5UI0o/lT96yUewwXSWPOB+CITSn+Ldypdd7tQIu4LCvLI5neBRV1gEe5MTyowENJjcFWvF/Qw84mk+WEWDSv1CAgJktCFED5RDlAJZatP8zrbR50HwVXHQUeTm6oWolKwUF/beB9BDQb+/wtjMvpemF0n+WsJNUJq6Cb9aXwL1G7xykkVvWWwVZDdt2/2OW5k/vOGDhOzRZg+fDEpeX/PBBjcnq3wL9lS4iStAxQ+AithWjN7uAgK7TgPDKsG1aQMsaImJvsfExnFChFA3L6EN/jWJsmKwX+8wUN1QlQIHe5bsbS1sq4eL7Rsxzh82ogkPFisCiiUH60FtorEGazJVfg7t1XJOPsX+fbVdgjKAk5YP+fR9qs9wumE2YnMzEUj2D/p0UygZkjr+j19d27pwYoLxOUF4ZtaHFj7Gi1YCA/5AACgAQAAAXKAUG/5OiR9rfbwBRv3axhGCxRuKmpOoJDh1MfsK8M1nXayOjulFYeSGg7jMA6AIkagBp2ov5f4mN4qlZrHA7+B82kaOI2Ee3mWllTmK86UAkvHs78V3rgqFmxZb3u7a48ZemhZaC25R5rwLeBDy8KIKzSkGAgPD6789u2C5tbmx86R5ery9/U/4NxCV8vPGjfO/bve1aasdX9p8weoedD/NEAJmYP0VXVWf3xoybu6Z1WL/YLhgC1rcw2cok/xQg4hXWUDk2cXAejMAFwDamYTIfJWh1XptPmBr6La+Hef0TH/OutpjKkAf+raDq5weBFhtWujVRvZSeRLUxteqH7WqGNeOv7wj3S/DtgvM6LRZTXzjvwlqCGumcbmASuHk3d6moNV4ArSHnqQK6+6xlKWvZyDR7w0P4K+rWCATAsZ1L9780+obG9S3iKYdHJdp0iU84uBvArAdxPZhZplPlod6YceXdb/56P+QHeQwGY0lIyILWCRV3pULYcYmxO0rGwX4vjZnFs21voM4WmJNvTpC34er9FOwvCOsTY5KqZ4Q0Bzm6nbsf+s97BbY6Dt7hE7KiU/mK4dIJFiUzGAvxOcm7SdeFwC07AUIckYdYBcXXJvYyPIvqng3lChiosqCapuUtbMzdbuTBJMnfZGOTAb8VbT3uQfPmq7I7nlXngzsXeLn84enB5iGH0IPT53wHy0bmWE3nMVMYsCwEmM3Ei2h9Oc8w12eQcJup6siiMhlKE4BMCEsZXypWi9ZgcU8OE0AgCGTutbDPYXTL3PBtjMDOj2DrwJIhviRM7K2iFDj6Gj7va+L+gzMpWhATtshV03G1LA812s2cNhwntwkbH81x1yvEJP2zJQzUTzv88IGsFtuTeUo+6t/g2aHdYpvgdKbZRDwpzuKgA935vXEonV4fm8+n1LYmwl6mtY4XqRLzKCLngZUBLtOKBJjKLbpDhrDaKiekCyPVEYReq4sqvnVdNoB/pDO7TYlLcNqtojA9zyTOKaa7ijlSZAnRNdVzZTV16z+KSCSEdHmQyyda82i4XHyn8JWhoIgSBhfOUJlqwqiyLC7KKpJSR7bXUpC+1CbLXXjqpzNcG5HgX2ZMBSDyVERdJoas1h5E9n6NG+VdBzfH3F7UtfPt9Xkf3qTXpFVVP/BmFxfxkg3YcYiWLXPlNQDbSnijvdxbl3iUbnv4qWS0/ScKeOu1leCpAtMqtZmnxlYi6Nz2HmRWmq/ZuSZ3nTwkAo5FRZSmKQvw5ct9JJSpCiL/M7/s7Slgtu6Tt5xF5qETvd6n4N3J3CWq7CbE4GkK5p6Kzj9sKA1JzTNY/X/2B02BCFkCFpwy7ftqHceLlLO2MQRm6KsA1iTr4O+t1yDuQ7uJBPFjpwj3B5EX9q/A5AWln1mWc8qzKAW2zj8V+P9A/zWUbr+6WG6wMXLSfyMXe7Q+pjNe+nsWfvFqWZEClb6q8FcLi4D1ZARzvHOyzH5KgftaQGdHppmcDTmbPexvWm2IVb2lAW2nG86UMDN+KCEMiTbpBqqturcf36DxCF/Vag5Bf6YTsab/V26MnrtAenXZg0mq0eld8U4JU4gSXUjDgIDzqupZgqedlVovUqxqWoxcZ6h5SapanrN2kutZUfygY8nY91qelva6uggyKrfbI9Y9qnLYIv9ZmH9uSKbttVWcwyN1tJGD4kb9WShhcq/rLES/A1gZyMA+CUJ5yrTr0ugw/2ldFszEpiQupUIn409NAy8cqSlU6BwMovFr+LAZe8FYmOfXBWbLfsF7dwKz6h6+IyiZunp8U+SCfIvelaBIW5KTkpFU6M+bH4CMeIDbe8rEk6QrNscOq6rsqLxBBa5+68u/mDJQrZy+y2XQYG/lp27pIvc/aiXohHXZl3AIdJZB3WYdzOFWnILbIX4CQuYZ+dfORpGn5agKeUMvFBK3mUTDuZpS/bLRjW0t7g5T60r4Rnh3Je32Qa6/jRR9AfgD12vY7NYbSy5qfAUGGRRCaOGHbrOfg/ooaUBhSxDLEzGXgzAbGH/3xJ5Hxei9PrwVzQosMvmrTX+LIXSOxAD/H3uoD1fvHhPrB/xWCmyeLJGmP8JO0MUKMX9tff7JCeWYfeYE3/8asjKcRKPq7KAOozWxIkSBRzHywMBoi45YEuUkrNsudlljUxMzkCnJqk7TzQFynfpBW2y9Oe+tBeUynKkQsQuGtvQ1rEGFpnrb1zq85H7iArDKueOHfyKw+bHsavFpqRsB1XhD8/wAoyJ/EpYONnEZAEZJYSDzCfDOPIkO+MqPbVgUwvRWWJaG+2+0cUe5/ygBrulg+VkQ9kqJTHjBEvDexzjzUwzITFSvv6Ddzw3kk3PURPm0bEjS25tzliapAcwoGIY36rFRW3W9rHgMF5skNovYY3owJqPOWNQy8KQ+dThaTYdYOiHvLMAr2MsJwLar1uJo1kqZSV0MyaJwY4Fg8VIjR2oSyE2QI8cAJNJN70C5Q7qyRckXol31fo4hmDB6oI/JurCXK0x7uNNrMhB/faWSlq+hp1po5eoqz6SR/XwSkZGhonOCNII4fxrjZcXVvvMyRk8uTSZNFB38HtWZjyWEl3+69aMRTqT1fkAOH9p+udUboEOVUSnKSmBGRkg6XzK0cfFGPgLY4RLRJULBcnHGQTVrCuKSv9Wkvrju5GBwWR5q4DjDfCD7FT/utW1GLLidwNy7IgiB4c3KPTdf7dSvC3aJXXktGwPR/CLr6A9OajmTrOmEh2umFUQsBQaeQgrbQTfyU1C+xTXhJCZ5dhaUxfKVRsa6x5F6jGDTPqj0KGSKPTCubox/5UtwBUbqvhv6sXBGWYYXxCljacIIPaJyIx2/Mu9aQ6L1mYMOxnqecF1o7HpagvFX4EvDoxAxEqzam5XeuNZUwCfY8nHIRbzIN4kdycuixhjA5Nh3tFlDzPs7MmAqEA8IYJYpxqPbdZXqaH035IiVtJytOrfwkwC6fCzaWWlR6vgmi1th3gLuDXeL265v1VnbkqnrpIUuwVNqSAx845EgJijWd7Uh8g9jLX0XgKIgd1gJ47ZqXhxuB3n3smrTyEWyAHA8KDH/QujgvtLpRat/UriHTWnWzJ4W0kKLUYDWir2p/g3GoVb1WnNXV9a1aGtVEZXJGsVdqasqwY2fi5K2na+UGpqmTi1hadqQIv7AxLE7vCGX8ARd2ioYhBdRdQiz2Tu6t1sX0cHCCJyM/X/6zqbf9jQT2glDSTGxxhGJzFqzaLDWP5T5lqocXQ7nVKNENKLmu77TKl4V3JLb2wxID0EJ8/GODDSqCAWKXYc1Mtk/f+cCe0pTWTAPCbB7Qaa+k5rKxsuwK6h7lkxvw5xGKPvEdDdG3siAib7TmvkZhy/seSsUwN0N9NNNZyo6nGta+Ne5LbnmWlaHqbvX5JNAamRPgxj5+vRCI4GneDixVOpOWLq/yovQ6H06RRilrYa+1lhzJ4eNFHFLQO1Pe+CdZFtF/lNvBPIrWYyAvZWlvi+VtdAiNt3UyfOghW6TUdNXn0eMn34jsbjDeWHVhSHinWMWYDzqlqrdVxW1mkeiNYOYZsNNQWPoTgBviuTMCATQKmhD+D3xjmEs7e0QZYkkVd30pCA0+rDiVhuCfftECL8Fid1MbFq8QGqUIjwrA2G9qqxSzqekAHZqExosuz4+XX24upg23mfrrAd4rTR4jUzir1ro3hV6Ia8Iwu7EXe998yZj2Kr1EhcPmykZCLOlm6hip/YDLGGeG9mwbs6NrCaIBW2P0HruI7DiQ0BMwNzHVlTNHgl42sZlc5pJAn30tE/edPKheoPKE0sJjsmeGC5VfjC+ti4nN485TkLDZTmSqJ2u87h2ao91Tn613zqy7/uK7Gh2FMvCOLWD7nOTIGksAfTPsZvZxjR2GtdJD9+mUVIyJnH0t4lJQDlWBVgWmTVLCpGcJGMOiBrHjOSz6qO1BoVVYFqfiNCucoKeRkaujiGzzTZrveHM+7O1Wa1b+WnkS+iEB9hx03flsgeuFHn0dBJxqyp5EKK/ho7XhotHx08+NB21Wpgr7S/ltECnxNXaDooLRuFcTaXv+SmOGsWFNWinQ8UsUy97xnH8tEB1xGiH3Bnk21CvOZ8e9Yukh3iWGesf2J9zExnhWrnaZyslsiDzTrlIx8muZ21TJbMmWBnugpKAxkKvN0HXS+jpOLHuX6niou/Itj7UUK5gkr/KRRenLBBleiV0LievvAB9YOTYlN7Jw6CnQbUNqDZjSOEG6KxwKSmpGjF0socfvx2Mm2a50pzFGL3en6+70HDA6zD1y50m6yCKtoLH2/UrGJR5i67OG7QrR3tmOReI/yY+Wa8OCnme5YIwqtS54fW3hxtQx/gmfLShqHzQwO4jApzSNKWVntpTRp02FngPGhLd2J5AsFZYN/DZQUOtX+ZHLwreLyujauU8rlmk0TjNYfzfC35sP/0Lgh9lJTBYOG1MuLVsww7EPhlr+52H+rj6HeCMPeLmeG3wwSZCs1Szr+M7Xvp60O32lWoeLUy+k0tjTooSnKivUYOrK5DkWkSCb2bSUE2qt5M8N1sOf5lxYbcVJ5wF56b4pibaPhhGc+Z4xBSUjUjcXKawY96ze0YynHr9Ze9p0WGd4napyROD9f825kXLPoncnc6DNEZovHj4ERFH777r/nbT2Q/dnies2ctT36K+SAI7uXI/Kdd31t2YBM2VoOrgimoeBhBdhTjmoJAUiH6YLAJD4jdh/1dTpeEnDLJAepfUNtbV/aYJdu/PzXtM4DDo6SQjX6+b6p5q0yPuuU1CSs4XQ/VHarCodvh5LV2MgCbA2TSRGTE8lLgHG6vYXrMg5tnCKbGwWdAYZIbrlPKHgnDKsNZomuu5K2L6IeUZ/IMUm/JzRMFqyahkDjUu724fVVRYiDYAgJ2PnHg8lqmoketPjUcIG7HQKxDoCl+ZzTRmGXfyT3nCIfkgsxOHM1LZvktJHsOYRPlpB5AXemj3tykWKtGeBho3cMf9UhAv+SWeSUp5Yr6b5m7BtWdpzxHZhHtCUQa38DZFwUHknF7WomuwI+6lgpUU5hvnKLW1RWc6LIRRTQFG9HWA16tA4fmxwwwmvycBQz3puxIskGcI3W69BSlCibkgn/ay9JbeUdED5XAdaxSA2MUGC/pX0CCnQcRZbC8kN/oxeOLXZ6t4QFd0uTD7/x69o/M5Z0oP1M24KsL9oWdDW0YPFUQpIcYe/0qp5v8RkqMCwl6wnPZKgXvNKbtALGZv9tylSXCvP6lpxQfTQJs6NJYr8IMXPNT6HdHhTqFhvPwhDoVMV4uDLsmhTNofFMXyAII5SMh4oISEMUwYwnCIp78mC0ILDRLj6/mihkUtgFkbxqx6xHg3Y4TxJB4ERaemfouxt5KO1i17l/G0dF3jzsAaxFtoCjuv1/kDxmXRheUH9g695X3jtNVuBvh25qwJXdz6VAjbWa0YNV9CUhldpsPnUAQNueNEb/YtehYGwvvU/Z4vvGbXCq47pOmZI6BC71q6Dc5axI5fAorR01JBpRhaTZijc0qHLvQlhZd34lWB9bTv1pDL+ImfcenyPSVAqC294yLuhpqiB94Qg419ZIJpovGcuBakHOx9fpz+NsOhsb5DUDNFJ1XspeTTFmU9/Hr/gbzTMSlt0Ue/AL8LBusqYH3C+Yc77WGVlS7SFqzQlBpMZWwsoXbQg+vA9vBWMI35evpR8nD3gXx5pfSSVmDi43Js0Iq+KfV2WB3oVKXwSrsV7jyMoKF5nYiny1Bk9GRuii6xqRSI0nK38a6PI2eNt3/5qyPlW7NqZO44H0ZnXfywyAL34Mfp1q9Rh7RcWeSozirvwknx7jnvZ6if5VfjWVQMTGVe0B4p5A4qyVoEk72olbqbL+YwkXeIlbglN2kP+kepOMk4hOFzyRw8RCP/RL7xbDR0ZjUxfn6RS1Cnd0pN2vkhaOLCvhmFXAcAX3ZOJL8Jqk7msC4Jv70X1JfiqfnS6dP45LHZ07XwRA64NFkL7FYf4vrYgpbe7s35FKTF6ISK9xJZla8UbD1AoJCfDLx1St0q/s2RIjtUdNLcTjlwPtl2hwUPpk4UI+i2JoY1XnFMOvXu4k7M4UVkvqmrQQG6QdwrO6Nbeb0yuye2oCEYvs77ms95KdEp/W9X3ONbp/0l+0dO/hVH/3UV3jHdXntSlmuYLcwz29HQUdbMn5gbw2E15zvI2+wK8IFJV6Hvroz0S/DaRGS5MZO5sFXnrrm0sFdIspsUZ6OR1sLPTfzNYTi/IHMNKZ8djW3Fiyu/8A8cvAK93GqACC/TgdWYga8quQQ+Y6rRE5HG9nG0aTcReiOao2V3+m3i/F2hFQrqSWMtmirCmWS2srarS/jj+Yrj8RbSe1kG4u63a6vTgvmjF5A9tWYAOBvczdXy+q/3vX5hVJ9m1e6UhpinW8w1isdM/BUsJCJ77AFFmSG2LGpV4S8sekMUb4hklsbKyr1+6z99JZhdFDBDGucE5lFbWpwWHa2eVDhuJKB5ukbm0lOijXS7pGgtIFnn6IZS/p/yArwvYlp5BiCqj5dppGcTWuAQ3bclFb4JRMS1FYWHJyaNpCrD71/z08HzqIvYmk/7hydfHDnzjERvgv0gBeoD3BijgcZa4mx5mgNgQsMaM4gnhbL9IGR5gGHBDoTEhlkRA11YqN+OIvAH60rszh4aiIu+HMz4netIG09tSBv1cXTutlPRpL7b9NrgsKoeX+zN9VxnaOvmBWOJhDkehbHgh0FeT+axKvFxYFUyKqNZD1wI2ADy6vWFaAGrfo+2OJMjZelPp7X077emxrySAgCzs4LZHOYHNf+GSoHEwlj+ommHqzI9lEfq2rwXoRntcrdLyMyq2N0aqugF1BVyxNcfFNKYbr7hoNxhuGr/ZAYe6GMm1RWQ6VlXIBjSfPZZVIJaP1efGrV69/E7GqwQnchDqYkH2Me8He6QJM19+ztXgR+tYpCxgtKaVndwkSwnh3DUNS5xFu/gZ/enh9a1QDgDclZc9z8JzPwO/J3kvSnTxFxjpwRQnwlfwb3jiXcEwX72sBPSanFImhH47F3f2G5hqOd9bNpHkW2VAoO+vkWm289RYbLy3pdc+fB2jjrNSjfIBJuL671jDFbYebNvzB+6HhzpQaEoKCyi3XGS84FbUvXABjeEOeAS1cGfOGszXXMx8EF+Qfc1S3b8vYcvGHagkAGZ0zLVQHhNYn3nvBkS8qniR9qIMBbvm3cWZMBUruJyFW3mJ3MYVHk9f8rGjvBfRYLIp8e6718y5zLiG7r2xUOtBE6LEds/WEphhZFYM9AH61kDHj0XMzKVFkumu18L2wH2/JYm4+FoftP/VT4xYR29Iw+/7/jXtH7UHJe514m/1WO137x8GzJvD2ji9mm7kfvjfBIRAbP7iex4Fc1Z+PDLtuGzr2TLRHrzqa5SNb7+K60g6ClwYr/j87avGp8kB/Eu/ij2tK761BWoAIiAvFkj7EjoHhrj3q1bsbEZUOnKBsOKpHA/3AuY6ObAcWfoPiRqj+ooXb32pi/GRSeUdqzSFLcrj0v4EOsJRRqkbuZWMXfy6EpkGfDgg1l0wQiI5pIfrYsW70edmE8AoWRH6jI3fV6WCoXQ6mZq76GVczyQbn3EDRZmlKszf2kgVb/JGJQN2q21TANjFne6RA01aKXWRslyHbVK2BfHGBfP5od9QrsMnJiA4jPzZR4M7QbKmCWy/BlJ/wIJULi7tROrIvdLHwmid4sKfHpsWDb9zbuehIZLeHqJmZYt1fCu83CxTs2i9dAD2lVAtYLyAOJVMSgY9oLrpWQEUf8LygZgz7XnyJaU241s0bSOqpFnpNExgk2Zl+4/92/GnZOooJzCiIDf2fPA6laPHDXpd5DFfN2EfmMbACLid3OjksmHHM9T8x4P/5AACgARAAAdpAUG/5OiR8aAlmZuUq6KFh8XAISJ34CA6G1K/Vr7cPtc+1PuXPuvv7Tvq0N9rX2mT1y12eXOqc2wvUd7Rr3Q/0DH4rz7X1ELx9ebDubz8G3A3HEhXgLDSLV5cWxbLtvK91sa1P9xXF8ZFwR6Ql3wDKICypfwcx2xNAgJ7UZIsfvR/xIfSigfnDBbY3jaByZ2ppiMeOtiA6Ce7IweU7285ksrlyaTfMlHEZ00/EL10ekGvVBhqe32hEhcWycHILlpkPZp/o7JV3q22a5tmwm3w4iHYcKS3Zb3D07KL6kJwVkb43hzjIfnRSjwWwSKyANtWfmkZqBvpF0d+ASzQQTiAteKweTNYjKKBD0o1oFvi9QVaBKvNhoSGahnDn7QAC7If5DnFgc2TVe+Pf8xBXnB2ueDLHwT0+glifv1FQ0xambcXW02Mh33HVhcAjsnPRxYA37zPA7sf3Bys+0Hh4cMmbII/Y53YBMIUuwMXYmBZw3eSEg5n1DiAdjHZ7z8hTceJDt9IrSeIGQJvXPxr9ZVqel/xOOlyqOaX5oV2PWxtaDLQIgUfMfeW9WIE5LLOFzMPTYnOupGZjqesj5Kw7SRgduRjpMxgkK7Do7sfEq9Ja3ShL1jfmhmkUbqalWkOXgF2iJO9c6EOmVe0hNZuKdqCwjjZD5pBGPYaT+i183oKM2F/idNHSfFNODQmmy0+8ANny8FycOb5tbtlWedtWkNLSne5dghaHOl3sAgEY2hydyHB4Lkkd373HOOen6UlAel8poEPSV0uEp/8shLM/cesLd5zPURfhKhQzSOE/fIS4+wi9g0tI3151pZyH/cPY2UwKhs5B1aBKSiam0/BRdx2oSLX0MP0ZBBLz1OmyVkw/8fgkozh3o6j1B//klqGsVduiqRKVL2Tk/UVRlLLPbW0q5d5tMj+IsRHD4rSKnAIW7+7sxTMIBO1SO2ueBmBWHW0uGMmsMHScPfniTobe1BGhDsh/JbCd7hL3D0lm+PrqyiaNoPjKqg5ixbJZwKtKfETPYAPSA4q9vzQLxiRfOPKLkp0S/E6WRFQsEbjv5P6hXrh8YHbzn93kKyueif2QZKjint4U+Q1K19ZzLyIgj+12kuFwfqigAYmp11Ai2FE39w5MsbnXi0RGGJn0ZobBmQhBWGVqQmLerRTxJOk/g3woi5s5SXLs6gOBIzeL3AWaW7yRZ3CW5H0YqnZMiMauHw8bz52iaXtZ3yRPmAya75O8Vty2CfYoAU2KngEnN7KZyZ049KRt/plG+y9ZufBjBq/dlpgPo31QDkLGyZGVkIu/et6lrtQ3xFrcj8JmFKRwWJBJDCWJafUum/er5qbCPVtyVzF7Zx6PjYWOM2YH8FUurO5f3BYEr3DgZPwf1hwSZSOLVDh9NsTfK0W8V/ZBVmGaShpxqcP5cif7Gf9lpMDVZLko0Ra3+RLBZIbS4eee8xQYLZycOJgr7xAiAXm7XtMIlnQYcH3RCARlZeHmQZH/80eWhmU9uoExlMWnjr+9HzUzgtzz+UQHKWLGjoZd+J/fsDGRKDNTjeRhwo6f1teV09cZ7/ELOqpAOFTr4sNtjCPSxHo0dBOPZnVtOkLaxWJAYYsvZ9pQP5rZKsJlulyyCY1ILnkOLH00PNrSH7hcZZ1Eyxd6jy8294pbO/YIsxl1DSfdTs+IB+jubRT5WVP6kKxnYNOPuzExfO/UjtEAqazTiKC3Sv8EWtXszX8HRFuPr3pBUhLQ/0t4YNwXmc8kpJtkJNiDZNfva5z0sh5Q69aePF6fr9eIRrpmh3JIdjsbm/fwJP0Z1jo5LAdq8PwzcRTZkWHlLOI58mZS3B8ZhzMTIGsOFA8eFKlyUA5kOAY25DfggQaWoHFS1Fv08eC38Rec/3OnewFYMakk7KxMuSoZolxKe/5KD3/owZsHHuurkX+KT9xbj4QesTyb8wSyqHaqiC4brPz69xoS26jE2cojgRsx56yFET3QO4SMZ0avc8fpkQocbzVcn/f1rDCOjMqLGLE+8/jtgX2sRvoN5c1cKSAbGjEZydfO554R2Rg3HfE3nDuMQ7bCSfSwiMlBCzmIckH+ar/HdvH23S2GXt97rhfxDbBn1rYd0RcXWD2QQxCeXUA83qcNH7HLECtQ3JMubikOYmBm4SWCso7EW6/VXSENNN1VpHkoOD3OldeAqUfUdCXfvYfIQcjLtiDsUPIcRmUyIpWxBdyiiOCaGioGNxfY227jfEOUx+Y9oumeTmD9qA6fAdVhjrn9etiqyIMsQuCq/XtgdYuSYM6jREtj7S6K7AwI9l/qDrBz0etvZHQ4oyzmknNUSlqzN2GqCc/3gr+oUDu7o9Y4HHVC+BKQtcpQx6/kJOWLMO7uo2NGV+QtbX0agvl/IoAjmKINR68P2P03A4t3Sqw4DMPpAA2oZ2bkAQA/m1ld2rLy08ry3JWNcjzy6wZtjz1jm0hGM5XGsT8q5zD5xVN0Y167KtSr6rqSrfaiVov7/eGU3bdv7itkcpSOLAniq09P9HhIshQmg77YjroCxh3VB1K17KJMVG8lSnTs4jfJGLauHGvGMfs1ursaGJM5dUKjY9A4f0PnvX3oROh0S7bYILE9n+RFrJkYzz8wwX0jhgswzhTvNEEsCSlCEcedHQRHyZraPOfwb3htJ/m4S4kbdHpSBmp5MEjgfwq6GaTNHUa3pOh4lbBO68pJ7rk188HIlvwz/MC8FqufqO/AjrUQ3GDwwP/s4gAgjXk8x/kgcpi06Htn7V5j4alZTIGvuj/YrDZzqr+kh3Fg4QypxgceX96fOzQ6GxaO5/SvZ4hVE3M1oX3tzrsQV1kjSSz4q75+pjfh5cFpHAf0xhOnuVGfM7EVz3y21IACTNQW7Ll11JFZJhpA+qjkvXj+m6U9VtscHW0OJf2x46GpkKPy0QBVUln+s7tM2Kg5q/e1lD34UKkhlhRbuUd1QdFX2lEWekcmMkuVzjRzrgDrGGhHUXkJ8Z1nuqbBlT/Gne1FFAZeAbtsYbqwe24j6HR33hQgRg9O7G7DXoKaul7VheRDEn0raCGgK1C1O/VxfIi6X5wM9oZMCDeCK6QprmjhkEVbSyNQd++WXnvZ6GInE2znrNeCxCbjjRNwyjPfO7uZuU/2if+V9s5XamVFu/Yj0Ivxh6lP0NPgLEDGz56c7jzmRvnoVrybntU20+UL/+V9FKVijpBchO1MBGN5QXXXVedL++WZAI4rijqe9276m8vfuW3ZH8Fuq1/auva4ghHM+Kv+I5A/aW4KUzmpjVFzPho75e0WqSsd/750uumSDx6WualNvwUzf7sQDy2N4JmAE+W7vcedlaoUQrMSNCP5PZscpSgySAmhCUo7rL8gnw16XF4WTaMgVO1+LwOxoLvfP1hL/W2c/Dc0yjO8t0rGrWbR6vvGfgg0KovMb7WSzu1S4Zci+UpF6D5ePiVFD+iJhblcoq9JQVoXlN5NPKWJhhuimzo6WNHu8fey96WdoIck4U0vxsENFmMBLoEiGFQRULNnDy/hQVQ4CAi+B/ZWli/obF5166vJoBu5pIs0EwzbJdQRBLAupcttcX/J4Z1j2PdMBTP4vutGYqT1K14cZFVfky8E5XxU+ZsFGVCQ0KAlRr8pTiBi66As8CDqXJv7P0wP01tXbUP1K+BqXmIFsVnGVhZWWr0EhKq1SZ1iFvU8gWNEdPF37L5GwB8ZCqBCqoEsrPdWEGkKfw5EnCa8GZsFkmHCPuTjpoCgVDxuaPBGRsp1I6YmYKUrfRmDva8s1RAH9qlVBJqvWg94MYiAxhtYsZ2o1jQYggokr/ULFEMQb57Gp+AujGVLBwaCavmIgc+lu1Blvim9DeoE4PTcEEPJqBNnuKOgLMcuhBem0RJV/lNoHyZrfYNxHTqZXmefbfD1SIYkEI0b+XmvzldxfAdvLbKXdft6I6q7SfAJ4Nned4kH/LxQKrYDsYoSo47LzQzW3IGOS3mQ++V9UGW02r8gG9kfvKB3SiRB9RAFfEP6U4+BCnO4RwDGL+W9tK1M80/0kNt4cQfUSZDkfzy5rp10rF9LseFE7qJawxlqJFvqZF8ZIALzj5MqsLYbWxdodrtd1arlpdvmeW0WitatClzMZWFo6vqRrevyRLXx/llcqTTF6h1SqxIr007Ci187CYx6ROA9LLJtYTPGo7kB+mDifaQJSfn/XsbP9eKdXGAbH2g1IvpB7IjSC+qJStWf2jsuT7GDfKt7RRiG6+Ai+LPBNXXnZer8+82sF/qCcw2Y7Dc3JeOi1keR/s0CfV/dUZaVBC/eZHK0gMxGy047jKR16hnDVaRqXpae/xelJoGOX4v+O4VlCK+StMq1sggyx03g0yqtapwfVyiDYuwKgyCCSQKZigt4XYFJIUPGjlOMTtnG+8q0CF0mR1AWnwOw8DECikKjN9+HosIJdNSWgn6P86TYQXnArq9SOCF8E3cCS3uMkN7Mmw2ogv3OvHBfzMs9KS9orIEPS5EJUsxJy8I8dh3ACgZER9QY++vpBDi+TUwSdBIfhZNmXAxSYUk2Kqcl5Is8Nt78GGlysTcxQQ34cV23UgUSte4ZGCu1bYsmgnCmg20M4Htnw9RfQw3ioyJq4sbEH5Yq5zkbVGrr0FINDVhq2Y/Dh/Kp7oc0sH9roLnl+ZiZAMkbDMmViL+/GWhBibNqphmNRgBvFjXltUjLxlmKLlM09RAoLFnkRwrL5ZsXufT+1lI9WLC4eVZW068RIXNJCMiyq8pkm0z4ij2qJq4fwPIOK8CWPX7BsYnMFaEnw/8SGy3UBPt+aktN2ZHO92lvE5TJizR1uWrf2lkn6d5sjGnNvpiyA0Nu15RK0cHq7AniulfaYfy5VdXf2D/G0EymU1aVefGbX/cP4jH1AHAZpx3nKktjggIrqdWD2lSL+H7JsFQLyQK4sAIB56hup5FpULk9iZeLciRbfSNhfMpKr+WiCTbHDkFBhTREYD8mMd1Q9Nj/An6U+eoKmzJBWTt7FqXxSrWd7D7a9jAxLEh1S8X9lOJHpSR+ndGWTVR9L73Q1tfQKlUOBK7i1N0ELu4dK2VVZWUFbsuzxhVhFqJaDY9cGWtKwszpIuCFduwd16YZ0qUSGvQuuMlqMDOl76iKKiLwdHz1SmK+Pz5kuzq08taqruzP40GHxX0FAt9YSLlZiinTAELn7/ExeWeV9GdWs7ZxsNsI/FuAA6T8Ml8iWgwkgontsePxowA7gcJFtnVDSoBna0lPIRJ41kp/LmfVMxqlJM2MBHkCuV0UsrAMCAEeAHXoJHqRBLe5cCl+X96Sm5d/IeaVByC4jFmpRIqoYHsfT2v7clNmAUxW3wuwp0LeW3UB8VQZ7X3u8nu6BTRnagRjvXBOO7NytO4tGqcHh5xQGa4cWW1Kxd2bpiiMbmse3nZkCXSV1E82O44kmp/Jg+zJfqhUve6mmjtCTXofAEnrVd+WRIWzIA/NWu80vaiLQb2zQz+nVJsXZF+6+AkQR7thhS9PKFKmlasavbKWasaN1YPJJ8t0Y0rScAlXSOh2aQoEhd6mNf0/T1RfOpSxu2fhD2YTnpkANZqbrTSVpKRsl0Ehiald+PP5czOwaOBgjZUnxlHW04MkFqJNTISab/hthcBhT6vkHwy+6R/3zpB9g2wNFju/EfH0jHhXGKGo3kebPIhTx7BboVxBTFC3QQD0e70PrgN2rd/q6Z0HcsWcTxAHMYDfD81sB9mjtHOluNySrGjNrf5gVKow9zp6qUe4uYU5u1j+VFnEo3+HteOUyJ7VQjYqXLAPPsLf4pO9w3c8zTHn773vzCGBTjh5+UVJ/txP5vP/yD636HguQfuouyR9V+Pxm9ch9xervVE4lfeh6SdTJWqNqcul2raLAtFyrM/2N4x/pkU5fzG4l6HpDnWqKRkA4I1PjhAfT0HB0lbGnFZ2nMQkewk4vdh3lAmbMCQuTZ3m1PTrnFp8MPbyAR+q+wALFrPNCzYje4hkCGwiJp2HSHAWc7OsrqfkvKozij0W3F2KmnRPZCI28A8YAidJFp6N3GNDzRervESn8DfVjp7GbpAY/AiWBzpDkNwOILsLdhKKfVfEctJvrIdG2cD4hfdEBQFgZ31UqednIU4Stl9k0d2eGG5GdCEaFHVE4jzJx+nJjh0QeXF/8HrcwnSIkxjzXVe32ZjUKi8Y+FgDuOqrMizZqo8VhUwctclfZb5SAZ2q8xtqLRxkwNj7hYlWta3bHNXYgbEQRywUlawfUsG2nNzcq6RDxCDhDNFN0MvOR6GfryF1QyiKzhXNXqaFtUm7Xh6FrrAhza8lw/KpBUoyrHkIC42VRXhceHDiDn4dFIJ+0SxqSzN/cyPORD8O+2b0i2KPknWKlnMR0QyCHhQQmvHyuw9+MLcAEo2D3+1K8wzzBSGBDz8wWwhZ2+gH5nfELasFD5AMZBg8NNvb6WMDh8+B14acOWjUcDpvr0o9AlT1S5J9qFAcnGzbaumeyJWDIrwOWXgzFBcEYBqTfD50pjAbzdnmkBc3aXTcXDoWzMzAZVcwYeoCN03geT8trXZmpVrwWbPJOh/ps1HYq94T5qjXprPoXYR9eMe8jyh/dwkftF/PM9+x5wV+QUhas6RK0kVhRUnIyUo3ds4YbVmH0vgV1RmtZS6FdkwI0XdvUaqQAsZMwfF/4LeKEh9cUnsJFmnhlwbKaXElopzgwYAbYCrybXFRm10FT4i6a5LfusMR/Fb580jq2bguQANm4w5FvqEpe860CEWqJUEJbi36Vry7mmvw8s84n+5bzDOu74ox7clmd9f023czJ/lFZ9jIfNnMe9qeP3mzyE7/TDs35EcxIS1gJ/C8vWCQ41z5lDzDM+ihnv9FfgAAl+pPC0ue1tMGu1URPangMCEtqdHlcOB64H1QC5Yow4a9Ijgrva4FzSuMaB2i2Td/jPp7JdxEvemuSvG2HVsZQOzdY7KJmZhPdllASBaAlZ9TY6O2NN4a7VpPqv4NojW4+hWFwhqK5awwh04sYYwmQXqLUmaQthjEBjKlz2MYlqGEYRwjg5BlwR6oIyQrQv479DAVdyCfakjgVi8efhXeI9ZzxL78Bh2Fez0M6fB/PbEoB4KPnRL/4aYwqiINHDmiPxnoaBcjtjujcBI9roOtISsrwppxuj21NDd/yVDL8+I7q68nJc8QpmbadY3jBpKyoqKVjF8EsLmb7/PtDV1/lFfGmOejlh6dT1hdNeAgMJyKEtAqjKiJJuIUIUq0gDVgpe4BHRxpmbGIb8omOsL4jrd7HBH2Iq4uYGTGeyq3/1k5y7QuKs4ewqyyqZNrhosKARSmKTHoEjcq2Ktg28BOY4KFqiWoFlyyN+DOKDjSW/tnowvlizJdPlAvJWfT7H9ThvjVl9zt/V6Ir9VVgjx1z+ZUaax+BgzrIKw+6UdzWaANJiU4Bp9RKcFe24xS4nY7KHtPMeozqfE1LlpTBm8MZtWE7GtJpshWuxaH3/UVgpZiHfN54bpNwJu+tPEvHLBIFngoYswe9OT8iTxS4l4n35p0ASvsxOVaewh47Tq727STEhUZL0IF5VlkqJeeObCpE2fFbj6cHnIzNuO6/dx7A7qzefj8GpsTowk5LL/2DlBwijuD3oju2aMiYCJDeG8wlmN96/GXFqWL6P0RVjDIYeT8XqUGMkZb4BB0t2ecccirH1yO7cKFnOOnxAI+8my8mQZp57N6Aaz/6qTS+xt6gA4ouOCEQ0ga8l3ZzeeXmLyyyivYE2TlOE0Ib0psOI3X/dUCwSKTHDBKiZTq3bLHsOzbIsJ9NCjBYaBiBdbUNAU3yqcoohi7/l74MxJLOARuG5L6bqyQ6vGOhn8ckSs6BTu4SDYd1YHYq2u4mQGNG5TuH9p2ev7Y1CbS4gNqSaz80BFL44kaU9j04zv4n81zZXKcvzxINd5tScw/Q115aLnKD2OYNeD9rJ0iFJedFTHHcPs72Q+NJKZpKhGvQMMCYbCxRIyNptfZ/P6tF7z7nzBoYU8lpvEbco58K9LS9DP2Af8RSWG3Gfw/8IBjOAFqPl4Eyl4sCtOzA0rE3C03Ku1sB6vmodgu1F0yiZcTVmpFIUtmwEjaTDkylTJ+IDtSor6GZSEIikffFtG1hoToKdM0DIINqS4mff1UklhW2KYxoFNfVX9Xsbs4Scb9Lr3DMZQejOVXMF676bq1ycv4FGFYcNeZ7sX5WnQvDejuTIrg6fOP7Icm5yTBnCeXWPEHxYBMD6gACg/B7xm0Vw7oQhZ+OKEBEy+8rNJNcFC7AZaVmuF5hC6qsSYuvOL9m/+wuzPHjVX9A8JDItchznoNFNozkzm6HqdNuKCQ0BnQCjDGsHiDC0sqDEBHFrgmRZVE5aPD8cxakJ6Y6IYKfam0ZCcHAA3evBLgHg0wMyKTNs+Nz8MWtGoT+IrYQJy6+rQz3KLyk//bUJEF9hMXVCy+Rgsj6+kKjxREY2igP/FHbvFEpb08qp1tc9sjNzyUjUIz21vMGu1Q4FVmPI6Nq7dJc7VGBtup1ZiQWiDvAwEgmaTU3a92FILfMTILz1ZAonnw0a8x/ABnP5iLBeTEuLbf9E0XK/nGa1WQaXOEsGadMeo3OX8qCog+GMzXpSb+dranI6b8O45+Yd6CO/vY0Hp7atg4vz6K3wGUDiJcCTRrqkS7pzCqvFoQwVK59QdOkKnvGiJrYb/SEmeWkI+fmPhQ7/QHCPqYGZvnY5iHMv9tFcswwY5pgIqp8aB3+ZmgYwGlib5kg6q9QcoHD0i/r92BwvAUl071rdyMcrXTjWqukNW47pO4paljtU3kO0VGdDs8tybLsxKHjFQt62FbZ+b2u40Y/y/JnGsa0Z6i6QS2BLpNnKRcFlG+67eyUkz46gYL/YSGrBWpEfFak2qoa+oyTE/e910Aqe9XL8aIPjsjOGwcIGV1PfG994loi2ke+Y26UPW3wuqBFoN6c+rEpOXhHRmIwavEksEelWuvaGOPBtqzz2NAUpDvS57YEv74OXiWdXxLnt4St3OTGKP+SLIxGVxIXwFr6U1BN+ADjlTD4Bd1NUB9hulzSmnVbbKFHvO8fomY3iUVnCccj9e0H6bWO0yCTJh9d963tcB4SXQoCVkPUdurmzQX/JUnNIA2SQ5wd2AZTOjr9vuWp/wrmywgF4Nu5T8dmpLOxONqindr3HOKjgBoA5ISULfDCk/zHgVzCEZEfy61OZ2O0lNcGqtXU5wnbljRXKOO/C3uA8RA50UfMQiRptTjpy9fq/B+JA6QNC3QB34LTkaNqmLa0OB+4SdczeaBfwHGxf5JB0JP3vjV30+6mXkCThn67n1DVX7vyhr8OgnXOx2Lj1ctCKbRzImeABrRFoulQMR32+HaPfdQl9Dg1j0RnaQ1WZ8hTLatY4p7aMYgXsOCVga80NVSpCvXGAVD+XLzO3F20L+o8yP5buXdwSyK+j4TGuazXuS693DClHjCWxBT5g4klnJ7MIU6dNdZEVZJ16CS/xKuinAWhPDy6oJ62wEvhSEQQ7fm09dXebf/KwH/MpnlNLFVfQE5vq2MlV1muKNm9/Rtp/c0kcjEP72GKGHS6h7t/oTfMuolZ1NNm9GhZVHB2BVu+4a6jfkgZXPp/tKDAqaDwNtj4Rld/9rPiHsRoRN1/NoBhv7Jc0yT60c+AJ07hAeABLJsLsHDsheFFYKc9I8ebXt/q2m9zagXvFGq6T+Hc1BuiKTd+xO510ycWsp4EKDe5X4vM6eKNTUVZ1AReyCYo+XP+DQDgvGh9jKtzBjIW3mYpJ3qABh8s4snYlVSc+Xh2G3WaPotfkjeqNQQVRqaJDyLvuWgoC3pmFzydLPCdvEJCC0lQUrMetIaSSD6eSJeYh5FKrMyORuvM3ellJlYQ2y2maBPiX704kOdz28wQole4VA2+RguqntWkXnL+RbsIiE6hi8aIQztay6eY4MjJDUWrDzoGDRF56jRSb0eaaABYqHFGJXPSFUACEiujtsq1YelhcwydszUEJSazHv41bqkTXO9EuqXjWnIZc5bnXwTZU3PZiUYSGkKZNqG7xvddY8NQKo2bl2f9HQ1cyU+MQYxBeKpYr+yxkyAB+lpRsng5emFvv3xgbC1bzjGEB4wrQnq6+j7byeExBwv+QAAoAEgAACKUFBv+TgICA8P4NGtvH2m2qnC8rm0X9YBiD2+1n14DB6jlDq9AdotJ05bjg3nAVubc9D1NdY4X2hX98QzIdqpfvTYQlMyV/bZ0/AZrQK9VAUs35GfMKAaEFpYb6J+iqocSt3ufvWRYh3DjO81USfus4e9O9jFuzPquLJK8SIq8hLDNBLk/T94BoJyGPAoYQWvhEWiHvZsykeIdoUq39EpUtixojj3HYJFIEnyCtVK/+Rr5t/UOAtlpFEcvVtEc6eCZXeAZQeTv7PPKKLrm81wMpvwaihf3UomLeDah1+8i3BDRTuWpUEBf3jPHvWapSb6ipqLcuKBkj3dThRmT3OI/0266/qSYKZ/0+mfPOhuye88BI5Rz4KNH1w9Is4KdVTwPY8fZEwZRqwhkIYStFLozVEI3Mocgh05R6er/d35HCshRmnrwxf976KGKYVvqvVTmUSTUioTqVW1ohwhRKqgi0BMpBTdVM1WJ0A+cLSBNxQHlY9PThhsqM5vXHMgaffsFkUuRRwlFfFreJffmK/HRLISLnOWmUPW1YrsSC0j1dUcOWIrckaFbI7gEovcEFko6T7hChmBfOFlKGPg/KWRMbTlOAgNcZgzDE71sBw96lkg9p/XmU8WxoAAHK73nWDrzuRqDC/dH6hs8rCFJs2sfywCDSM0vlioFUBPBusiXaiXNVCM8L+ADCQEeNydX3Php5Hta1+Zdx5ZhaTSzk9mRsIBj3JaOIo1ec+e5DQfbFkFQDeQLZ88qJ3cMSUY//CbGoznFXoiFlLRquzeUWTaeXkBhmsh3oRx1lIh36NwfOs+NpQC2puVMjL70IH+MOj/facdiGVa7yjqZmJBm8G3VxZCfqzHtm4yUqIw3bI8pOSxyZL+4/Pa12a5N407ZFqRAQUKOW2gfgd9DMqC66+OU2zrvVxU8Aa/Hk90T5jGIpiRi6Dy1xqgjAEx4y4Qw+NCLFpp81O1m7zg98ul7T5LF9AxPHpSGQ21ps9hpduwrKZES5WuYBlmFNke+0WY0j4Y1raDkg3fWuXA6psWuJCxFlmsQ2JWN9x0vWlheQWeWzEwOwjD6Zmd4sNA/OTVeizz3qu+qfReilNUHYFvhpaV/lQbEegFDg9c3ZkSAuv4qr3oL0HyHaSTjej3SK6odiH/0lyGmEPO9KgYCA1ktYzUrUKgVgDIKi0wau2lWu1OugjUny/CflaLBNgKOSpZ6Lk/bh2IpvAqf1QYO0S124db1S6g5oTMpgO3e0F2LPGLla2l2+V6ZiPum/DS8ZjiIXOceOskMoi9CU/spUdGb+xPr7rbUaVjRSQHUhjqg9Zi18jSngjPaBDQqV65miSPgTZK+TZMhQhVN4BxfMbdxHHQqi7kUPkpmRchxIvNwCLdF6VIxAgj4cQ8A1fDuKy4CmFtoPvsU0I3RHPmi31k2eORfIfkRNSOzD59CHi70X3Mqq48KaJOQJlpRmukH4rP0LKHbvHJnZfxpbzZNv2Avlq2rMnTqqWsWTxUQ8YaS814vXPXxw443LOfF0jqy9cozS3HZqohLSPqizJ0zF5VtnzFH7zjFP2G7cEneH9x3JZrjyBAoA00QJCHtyG2hN6WNljoRtULtt9nb1Mm2NFXTzysK+xKW7z4GkW9dH4NufyLXCGxL8M2PRz+q0YLFPesiYkOGJGh6wTqZOjsNhmx8aENQAkDe3dEPS4kMQrD8ZyEPXRaz8ZiKugA9KcPg39JzT8qoG0oT61kD668ghzAYbeUaIhj0z3LldI/9sDfK8LKsqA3LUgQcNFilly089TJaUa4jkBh3r5d3h6+dnLe6tt5UFYGfMrjTyiSdXE79N8CHL1WV6nxVKHVq2FqactBjfM/T3RhlMhKBTG+knp+mgGAz6Uf90QmmUqRFU8ohMqabYzDJW3HN1s0fiFvK4FN47f2SHwNASbIRI6sTHI1tewpGeFL4z5FL9XoDNebPfaYItNuS//V9RTJR2s+7PFZdxERniQQAM4fhQqbb14FnB6oCA4X4XxmGQBnRCBX9bMMcwxECwiZF4Pew6vJGxyFVWylhwODUXGPMYSxy6A/nnVlOgXh6tsdW5tkcudhq2+lJ8g66Ou2nmfAevGOUczKOI48xzGgxLhqztzzj+UBY+b58YhycF5pCY4HjEDXvuujfXRgwgRoVkFXxdiUITZlXEDdP0QVkUZvzhvh7/JO2HySwRAKRf05WA1IevY6SF1S88dBly/hAAyIxDZLsfiRJkX2OQG5IuIiOzEqq8AtPTDk4ptv3U2nXADduPedAalwuWoCUb1lhoLkeiN3oL+hpFdwuc9gkUHa+x/UQV9GsRFpK1yXJDN4IS9leLKwos/LES4VI67eVlL8cl7EnakKCsqZt2dciYkg5lgzhgVwqvCyCN0yJKPd88DmpCWQapfUffs12SoVUwQ1x7yxXbzNUoLqTat5aTt5o06WFTY21qjLWMFZ6/jbmxjdKtR++AG+/m0R/ayq939N+Nb6u7rj3P6Kow4ikqrQj7RioNJFIxsv6GFq1C20iyZBU+/Fhbb7ZcnvZIZpDwbVUj54F9tHp6ZRF1sZ99ZaIYrxAs6yucgICaoMlIgamK2r8MAzdGAAC9beitCzdDcdxskqIDtR41bsybO+BsNktTyTMy4J0v9Mwa/luQhuEBR93n8Qil6qnIX7yyAnu9Vol+BfmRyNzd0AxVV2Wu8lYCg00Hk7p3qwbuo517EqDCFo++uva/Rs6Q+fUXLdEapCg/cIvgUlPAjBIpcUacG8ha0zVEMmbCs2EkzH/qrUWoesCbIB6ImJnjyNxumqH+eYizZhJ9jigHoedZo/4RTauZZcsOv8ocOdKUTmga8Kos0GzKNTzPGSvnr6s8on6zMQx2dkkvzdsarHKQWQgIaXIIEmJYyDYZtcDHr+iZLIl9qKaB1xU85FreuhI14tVADRcDtoCA/5AACgATAAAAIAUG/5OAgICAgICAgICAgICAgICAgID/kAAKABQAAAAgBQb/k4CAgICAgICAgICAgICAgICAgP+QAAoAFQAAACAFBv+TgICAgICAgICAgICAgICAgICA/5AACgAWAAAAIAUG/5OAgICAgICAgICAgICAgICAgID/kAAKABcAAAAgBQb/k4CAgICAgICAgICAgICAgICAgP+QAAoAGAAAACAFBv+TgICAgICAgICAgICAgICAgICA/9kNZW5kc3RyZWFtDWVuZG9iag0yMzMgMCBvYmoNPDwvSXNNYXAgZmFsc2UvUy9VUkkvVVJJKGh0dHBzOi8vcmVzZWFyY2guaWJtLmNvbS9ibG9nL2dlbmVyYXRpdmUtYWktZm9yLWVudGVycHJpc2UpPj4NZW5kb2JqDTIzNCAwIG9iag08PC9Jc01hcCBmYWxzZS9TL1VSSS9VUkkoaHR0cHM6Ly93d3cuaWJtLmNvbS9ici1wdC9ibG9nL2libS1jb25zdWx0aW5nLXVudmVpbHMtY2VudGVyLW9mLWV4Y2VsbGVuY2UtZm9yLWdlbmVyYXRpdmUtYWkvKT4+DWVuZG9iag0yMzUgMCBvYmoNPDwvSXNNYXAgZmFsc2UvUy9VUkkvVVJJKGh0dHBzOi8vd3d3LmlibS5jb20vYnItcHQvZGVzaWduL3RoaW5raW5nL3BhZ2UvYmFkZ2VzL2FpKT4+DWVuZG9iag0yMzYgMCBvYmoNPDwvQ1MgMzI2IDAgUi9TL1RyYW5zcGFyZW5jeS9UeXBlL0dyb3VwPj4NZW5kb2JqDTIzNyAwIG9iag08PC9Bbm5vdHMgMjM4IDAgUi9BcnRCb3hbMC4wIDAuMCA2MTIuMCA3OTIuMF0vQmxlZWRCb3hbMC4wIDAuMCA2MTIuMCA3OTIuMF0vQ29udGVudHMgMjQxIDAgUi9Dcm9wQm94WzAuMCAwLjAgNjEyLjAgNzkyLjBdL01lZGlhQm94WzAuMCAwLjAgNjEyLjAgNzkyLjBdL1BhcmVudCAzMTggMCBSL1Jlc291cmNlczw8L0NvbG9yU3BhY2U8PC9DUzAgMzI2IDAgUj4+L0V4dEdTdGF0ZTw8L0dTMCAzMjUgMCBSL0dTMSAzMjQgMCBSPj4vRm9udDw8L0MyXzAgMjY0IDAgUi9UMV8wIDMzMCAwIFIvVFQwIDI2MCAwIFI+Pi9Qcm9jU2V0Wy9QREYvVGV4dF0vWE9iamVjdDw8L0ZtMCAyNjIgMCBSPj4+Pi9Sb3RhdGUgMC9UYWJzL1cvVGh1bWIgMzEwIDAgUi9UcmltQm94WzAuMCAwLjAgNjEyLjAgNzkyLjBdL1R5cGUvUGFnZS9QaWVjZUluZm88PC9JbkRlc2lnbjw8L0RvY3VtZW50SUQ8RkVGRjAwNzgwMDZEMDA3MDAwMkUwMDY0MDA2OTAwNjQwMDNBMDAzODAwNjEwMDMzMDA2NTAwNjEwMDYzMDAzNDAwMzMwMDJEMDAzNTAwMzAwMDY1MDAzNzAwMkQwMDM0MDAzMDAwMzMwMDMyMDAyRDAwNjEwMDYxMDA2MzAwMzMwMDJEMDAzMzAwNjQwMDM0MDA2NDAwMzYwMDMyMDA2NTAwMzEwMDY1MDAzMTAwMzEwMDMwPi9MYXN0TW9kaWZpZWQ8RkVGRjAwNDQwMDNBMDAzMjAwMzAwMDMyMDAzNDAwMzAwMDM0MDAzMDAwMzkwMDMxMDAzMjAwMzEwMDMwMDAzMTAwMzcwMDVBPi9OdW1iZXJPZlBhZ2VJdGVtc0luUGFnZSAyL051bWJlcm9mUGFnZXMgMS9PcmlnaW5hbERvY3VtZW50SUQ8RkVGRjAwNzgwMDZEMDA3MDAwMkUwMDY0MDA2OTAwNjQwMDNBMDAzODAwMzgwMDYzMDAzNzAwNjIwMDMzMDAzNDAwMzYwMDJEMDAzODAwNjEwMDMxMDAzODAwMkQwMDM0MDAzOTAwMzcwMDM0MDAyRDAwNjIwMDYzMDAzMjAwMzMwMDJEMDAzMDAwNjMwMDY2MDA2NjAwNjUwMDY1MDAzNzAwMzcwMDM3MDA2NTAwMzgwMDYyPi9QYWdlSXRlbVVJRFRvTG9jYXRpb25EYXRhTWFwPDwvMFs1MzkuMCAwLjAgMy4wIC0yNzQuMCAzNTYuMCAtMTU1LjAgMzY0LjAgMS4wIDAuMCAwLjAgMS4wIC0xNDEuMCAtMjUuNDZdLzFbNTYxLjAgMS4wIDMuMCAxMi4wIDM1Ni4wIDI3NC4wIDM2NC4wIDEuMCAwLjAgMC4wIDEuMCAxODAuMjUgNTEyLjA4XT4+L1BhZ2VUcmFuc2Zvcm1hdGlvbk1hdHJpeExpc3Q8PC8wWzEuMCAwLjAgMC4wIDEuMCAwLjAgMC4wXT4+L1BhZ2VVSURMaXN0PDwvMCA4OTgzPj4vUGFnZVdpZHRoTGlzdDw8LzAgNjEyLjA+Pj4+Pj4+Pg1lbmRvYmoNMjM4IDAgb2JqDVsyMzkgMCBSIDI0MCAwIFJdDWVuZG9iag0yMzkgMCBvYmoNPDwvQSAyNDIgMCBSL0JTPDwvUy9TL1R5cGUvQm9yZGVyL1cgMD4+L0JvcmRlclswIDAgMF0vSC9OL1JlY3RbMzIuMCA0MjAuOTUyIDI1My41OTcgNDEwLjA0XS9TdWJ0eXBlL0xpbmsvVHlwZS9Bbm5vdD4+DWVuZG9iag0yNDAgMCBvYmoNPDwvQSAyNDMgMCBSL0JTPDwvUy9TL1R5cGUvQm9yZGVyL1cgMD4+L0JvcmRlclswIDAgMF0vSC9OL1JlY3RbOTIuNzY3MyAzMTIuOTUyIDEwNy43NDMgMzAyLjA0XS9TdWJ0eXBlL0xpbmsvVHlwZS9Bbm5vdD4+DWVuZG9iag0yNDEgMCBvYmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCAyNDIxPj5zdHJlYW0NCkiJxFdRb9s4En73r+DTQQISRxIlWQICAbYjFzlstz3UfdocFozNZlVYkmvZ3b399TdDDilKdpLtHtpD49qWyeFw5ptvvlmsJzfLDwHbdCxQ/1i3aSY3b+DRUze5Wa8DFrL1p8lM/ThjPMK/dT3xopm//jwp15MvE1iLW0LcErD1TzerOmB37eRfkwWYX4e/kpFgGoQJW29YFCtz8AbG0mQ2jTK0+Yv33g8j5rW7B54kx2ojOj9h3hU7+Jx58smPmXfaiVo2R/zYdj6Y85j/7/U/4dzrcBqB9S2YkayWu9/agx9Nc9jYsT18ZN4Dj0P/Gj5o0/3ma/Lsd+aY8bbyIeCz+zneExYkOQumPAPXp3me2kCFw0AFeL8ATWXqjhleMQ4jfb+PNXtzqgTbth1b+VEyDeEahxoutQVv1VfwdivZez8KBoFge3HASwv2tt3KXatWLUQn6fZnufvFY2JvbULIhBMoilPLvpwkA1uf/HjkinFkP/DB2NtUnahZJx7lgXXto3nM6t63s8Me0Vm81ZSVnT+DLOB9JHvctU/+dahSdQWRYT/5YEs8ghfKKaHWPfCUH6r2cmy2gt0v3l6xo94ha7bxU1hSt6x9/CyP1Vc/U/c790p8Pm3F4Rti0Ah2FObCnwSepLbV+51U6P1D4B6zwoD2oI++WUa2GnovbgOAX8DzBbzjK1ffg4DDK4NXrj9H8B6F9HxR4Lm3+tcwpvesiNUz+B7O4X0F78si18+K6zDATwnZCJe0IqcdsT7Pnp0XmT7DrpzTyowsJLSb0zNjbVak7Hb0e04+cvoe0Xp+2TZ+Dsxv+DzpvTE3CoISVi8j43MxSu9tHzxlOnfcTuk9K675VNlDD1T8Y32qin3c58BsD9I+6mUJqcA1M3NEkWgv41Kv5kGhw64v1yfIeBKkFKw5/ZY7ATaBWDnHU0ACWE8BSfQOs7rMlMWQR0PHLbBS5UnSA2gYIRVGl9P6etFlQeUlm60AUp3pEoASTaQpRiZ0ST3Db/sBx+PzK7aTwIxY4fu20hzv1OyzhWOThAViIgV3s5GkqFKuKWIWXE552URZJDs4U/m9gAldJ3S8gVZMmKCaKrPRgcZdrGdIUXTX1yTaCEq3mgpojwadNvXGwl1/IlyUcLZwio9bYrjOCeWrIkl6GrhUMnS28d8F5vgOnOuaUTUxM5xkj9Y30O7kbqWC71Rt9jSK8CLtb/9smuiYv5yoIqItJs48LlKLEdyjKUbTknJxMXLBJHlpwpzTAvLPYaDLpfNBNlAGqveXH/2QI7rn1JFelhfXVjlZoeKJL6dKKZPz1t9rEG+KS4a1w02aTStxYWhuirAzoMOEqOeG4hbPtwz1G3d4imumNZlBKBGczQ4HzsGgVl0+G0LHhYPl8SgIMWemTHpCpnVDvlvQSQtqRD3Mw2gEwJ7Sn60YW2YRVW8yIiHXWUtCCunPuGtrbdQUBh3enGq6vbvfTYrpvKOGUWQDOTBo2mSyj5qpjQEJmGu5BPFKLb3aYWQtQa2D4H/CJkNaH7oGNA3ZAeg7AZJzU7XUH6CNhJsG+oivCA77SLWVWis3UG7SKrTmeKgeTxU+fEWLEWCdWCttZtnXYaux0urbi83EbETH3MYfbqn5mttNZB4D/VInP6dafE+CUVxvPuxFc3t7M98cT2K3ln8cb1flaqUYsSjY4m7JgHuSLCNagHkIh7q38PhaPw/U44coSvGXcBrCcBToRL1TIwxJ81PNSl/xsqfFQQOP47rSX9jbU7OtxE7H/S84hafzmc3KN/kFQyTMB0IpFXW8PICwgKEARrnvdL4DJdeVW+rn+bCgXPob6IhhUi/seK4AB5OBqzBC3X/JqpF/DnbH1BpZX0e8Y2ia9hoPFWijc48GXdpcQBfCIh6KgrG5Xuu+wBJqDsQ8H2C4g7IXm2or4OGDdz9///7B/yFIuwfeqcSRPGBPu/ZR7FDe3pdlCSz1iJo3wiEXWQrFLv7YVZ0/Y57mtRpGSnGyXJbGDUytSHZnk2pFTLirRsT440E9HmmuY1egWoikr3fvi3I3/Ga5O1KdJHhJzZ9pNDBUjpj7AppV7QyHQyVPXY1pl7+A1HuTN+QjmzxNTGy+/Vp1MBrp3vUftmzrujr62EYIIAjpn+dAXaD7vOUPAvY7Ki6xUU607K56qo6A7TcEcUDtz2b0i+zo97GBIuz+DyRrRP0Il1p/Uf81TOaK+mjmiJR0mH+1np+zopavVu2tAjtwrUJC0Wx0SnihSi7jCLyItVtwl5ikF+5HD9Lkb7T3/wUGc/bOSDGOcqr6U+UcXrxle+iwirUEoLbdS/OtXyHZnexk42fM+4r/tTsFqq9VrchLiTKlGi5rhhEDeg/eu3J5BxWAWPjuN78TR8H+wda+yoJ3OGnSZnOqY0ElPJaSPJ6mYCeOXhuzBh3W6MJeeZph5Rn0WiFoYESDmMIgDy6PQDkNNwa3WXBxwAnONKudJAx/3uF9DFXzuEhU2cV5wdWH8V35zC3MeS9FVLlcmqgI80p3R8vCquogLaLB1GKtuPOImULj/jojtbxwpHzWh++SuLJV6o5a573qmeFrHKqeXM7E2jhoUREOJ8j8HB26fb7YgKi2tqc/VamBEDaDVGc0Soi96H4OFXs0ZQw0/5uo7fBUs/YSFaBpIn+lfqruXLgookCaODXVgYndE5k8VEeUOqJlnTyxqt6rbqNZoYEfGvlE5jmMe6iJGj36SVR6fqSGjekrs1y0IgyVlHxHUVsRYjq/SkyhevGLaI6LtDdgUwAL6AD1nQrQYAJBPHVgTPknElBoWZ0/U9LbINlBlTuiOlZ5b51GTE0eptCzfo2LVRpeM7JkJH/urnwBYF9Osle0hAYjeDtM7A4+oGTYy10LClj1BJQNl7oCZl+yU4cLGeFBbpp21z5Vonsl20FcZCp0yyK3dEMS1JY4Md6A74zQNHpg4Txzi1nNWaAsYw0Ruz/WKXEqM3doCSwh5Y6TOKAseC3Qwmx0OqVVyQKkkiXqgBGJIL1FA3pLieKImIYS/ALdmmMXI47TLaQvCQN0Q75pz04Dz3MHkKM51eqoPk7fQ0dFRsKZjjOsFId31QqT4Gw0RnAn/JkrxV6pia1ANawALg+dnk3h78NRNJ981JDAf8huW7Y/oKCAqc/qJNkTtObmKya3p02vrID78OG+3UGZJbrMXikMaqUEUhVBBbdBnkes4CTNBX1cqnuX68l/BRgAT/Mvqw1lbmRzdHJlYW0NZW5kb2JqDTI0MiAwIG9iag08PC9Jc01hcCBmYWxzZS9TL1VSSS9VUkkoaHR0cHM6Ly9uZXdzcm9vbS5pYm0uY29tL1doaXRlcGFwZXItQS1Qb2xpY3ltYWtlcnMtR3VpZGUtdG8tRm91bmRhdGlvbi1Nb2RlbHMpPj4NZW5kb2JqDTI0MyAwIG9iag08PC9Jc01hcCBmYWxzZS9TL1VSSS9VUkkoaHR0cHM6Ly93d3cuanVkaWNpYXJ5LnNlbmF0ZS5nb3YvaW1vL21lZGlhL2RvYy8yMDIzLTA1LTE2IC0gVGVzdGltb255IC0gTW9udGdvbWVyeS5wZGYpPj4NZW5kb2JqDTI0NCAwIG9iag08PC9BcnRCb3hbMC4wIDAuMCA2MTIuMCA3OTIuMF0vQmxlZWRCb3hbMC4wIDAuMCA2MTIuMCA3OTIuMF0vQ29udGVudHMgMjQ1IDAgUi9Dcm9wQm94WzAuMCAwLjAgNjEyLjAgNzkyLjBdL01lZGlhQm94WzAuMCAwLjAgNjEyLjAgNzkyLjBdL1BhcmVudCAzMTggMCBSL1Jlc291cmNlczw8L0NvbG9yU3BhY2U8PC9DUzAgMzI2IDAgUj4+L0V4dEdTdGF0ZTw8L0dTMCAzMjQgMCBSL0dTMSAzMjUgMCBSPj4vRm9udDw8L0MyXzAgMjY0IDAgUi9UVDAgMjYwIDAgUj4+L1Byb2NTZXRbL1BERi9UZXh0L0ltYWdlQ10vWE9iamVjdDw8L0ZtMCAyNjIgMCBSL0ltMCAyNDcgMCBSPj4+Pi9Sb3RhdGUgMC9UYWJzL1cvVGh1bWIgMzExIDAgUi9UcmltQm94WzAuMCAwLjAgNjEyLjAgNzkyLjBdL1R5cGUvUGFnZS9QaWVjZUluZm88PC9JbkRlc2lnbjw8L0RvY3VtZW50SUQ8RkVGRjAwNzgwMDZEMDA3MDAwMkUwMDY0MDA2OTAwNjQwMDNBMDAzODAwNjEwMDMzMDA2NTAwNjEwMDYzMDAzNDAwMzMwMDJEMDAzNTAwMzAwMDY1MDAzNzAwMkQwMDM0MDAzMDAwMzMwMDMyMDAyRDAwNjEwMDYxMDA2MzAwMzMwMDJEMDAzMzAwNjQwMDM0MDA2NDAwMzYwMDMyMDA2NTAwMzEwMDY1MDAzMTAwMzEwMDMwPi9MYXN0TW9kaWZpZWQ8RkVGRjAwNDQwMDNBMDAzMjAwMzAwMDMyMDAzNDAwMzAwMDM0MDAzMDAwMzkwMDMxMDAzMjAwMzEwMDMwMDAzMTAwMzcwMDVBPi9OdW1iZXJPZlBhZ2VJdGVtc0luUGFnZSAyL051bWJlcm9mUGFnZXMgMS9PcmlnaW5hbERvY3VtZW50SUQ8RkVGRjAwNzgwMDZEMDA3MDAwMkUwMDY0MDA2OTAwNjQwMDNBMDAzODAwMzgwMDYzMDAzNzAwNjIwMDMzMDAzNDAwMzYwMDJEMDAzODAwNjEwMDMxMDAzODAwMkQwMDM0MDAzOTAwMzcwMDM0MDAyRDAwNjIwMDYzMDAzMjAwMzMwMDJEMDAzMDAwNjMwMDY2MDA2NjAwNjUwMDY1MDAzNzAwMzcwMDM3MDA2NTAwMzgwMDYyPi9QYWdlSXRlbVVJRFRvTG9jYXRpb25EYXRhTWFwPDwvMFs1MzkuMCAwLjAgMy4wIC0yNzQuMCAzNTYuMCAtMTU1LjAgMzY0LjAgMS4wIDAuMCAwLjAgMS4wIC0xNDEuMCAtMjUuNDZdLzFbNTYxLjAgMS4wIDMuMCAxMi4wIDM1Ni4wIDI3NC4wIDM2NC4wIDEuMCAwLjAgMC4wIDEuMCAxODAuMjUgNTEyLjA4XT4+L1BhZ2VUcmFuc2Zvcm1hdGlvbk1hdHJpeExpc3Q8PC8wWzEuMCAwLjAgMC4wIDEuMCAwLjAgMC4wXT4+L1BhZ2VVSURMaXN0PDwvMCA5MDY1Pj4vUGFnZVdpZHRoTGlzdDw8LzAgNjEyLjA+Pj4+Pj4+Pg1lbmRvYmoNMjQ1IDAgb2JqDTw8L0ZpbHRlci9GbGF0ZURlY29kZS9MZW5ndGggNTEyPj5zdHJlYW0NCkiJbFJNj9MwEL3nV8wxOZDYjuMPqYpEu7sIxAURiQNFyG3TpaiJS93l9zNjJ+1WrBxn7Jk3b97YrlZfGWwDsFJbQX9d019ZgLAds+oDhp9DVgswBhppoOYCzn22z/5cnbq01mKAR4vBbzBieArYppHAcNRclkxIZbAEpSqMNtwIA9shqz4ODB589gUHK1ljUUVtMBFBKmnBHMUV0IgCll3GIvGslJPSqusYcOj2mY5BTbXw64YsF6bofmePHYrDrq7NMeg+V09zfaTlSTAaTNRSUvL3fOXHy2F86cFtC1VyyP1wcuMvN+489KHQkF8KCXmPm5Nb11JPKCh+dJ+wyDteSmGh2yHXyZ0J68BvEiqlnmFwhwCHcU9bfx5e8axr1fQBgt+ci4bK+PAW87rW4kiAy2FAhCsM5H8Li7XGV5oC9AMMftcfcbnrYeNCn9iqlfg5HeAd8YIxZnHWOHWrAPe8wVknG/0yWYE4IXA+TH7cc9tqrExZ7xlbqhShNTetJH/MVslyzGaP05r85laFzxjMS4xz3YRo/zuTRSJIMiYrWxPFrCZLrYiWs+RE0FPqLYFJ6VztqsO0DSxutKQr0si0X6r7EhEnbicSOZZT7nyC5q3TSv3Fvm6v+/7We7pE/3LBV0X36wKkB7Ud/dE/H1ygF1HGC8bn/0+AAQA4V+J8DWVuZHN0cmVhbQ1lbmRvYmoNMjQ2IDAgb2JqDTw8L0xlbmd0aCA2ODQ0L1N1YnR5cGUvWE1ML1R5cGUvTWV0YWRhdGE+PnN0cmVhbQ0KPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iQWRvYmUgWE1QIENvcmUgOS4wLWMwMDEgNzkuMTRlY2I0MiwgMjAyMi8xMi8wMi0xOToxMjo0NCAgICAgICAgIj4KIDxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+CiAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgIHhtbG5zOnhtcD0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wLyIKICAgIHhtbG5zOmF1eD0iaHR0cDovL25zLmFkb2JlLmNvbS9leGlmLzEuMC9hdXgvIgogICAgeG1sbnM6cGhvdG9zaG9wPSJodHRwOi8vbnMuYWRvYmUuY29tL3Bob3Rvc2hvcC8xLjAvIgogICAgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIgogICAgeG1sbnM6eG1wTU09Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9tbS8iCiAgICB4bWxuczpzdEV2dD0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL3NUeXBlL1Jlc291cmNlRXZlbnQjIgogICAgeG1sbnM6c3RSZWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9zVHlwZS9SZXNvdXJjZVJlZiMiCiAgIHhtcDpDcmVhdGVEYXRlPSIyMDIyLTA1LTI2VDA5OjIxOjMzIgogICB4bXA6Q3JlYXRvclRvb2w9IkFkb2JlIFBob3Rvc2hvcCAyMy40IChNYWNpbnRvc2gpIgogICB4bXA6TW9kaWZ5RGF0ZT0iMjAyMi0wOC0yOVQyMTo1MDozOSswNTozMCIKICAgeG1wOk1ldGFkYXRhRGF0ZT0iMjAyMi0wOC0yOVQyMTo1MDozOSswNTozMCIKICAgYXV4OlNlcmlhbE51bWJlcj0iNjA2ODgyMCIKICAgYXV4OkxlbnNJbmZvPSIzNTAvMTAgMzUwLzEwIDIwNTg1NDkyLzE0NzAzOTIzIDIwNTg1NDkyLzE0NzAzOTIzIgogICBhdXg6TGVucz0iU2lnbWEgMzVtbSBGMS40IERHIEhTTSB8IEEgKE5pa29uIEYpIgogICBhdXg6QXBwcm94aW1hdGVGb2N1c0Rpc3RhbmNlPSIxMy8xMiIKICAgcGhvdG9zaG9wOkRhdGVDcmVhdGVkPSIyMDIyLTA1LTI2VDA5OjIxOjMzLjA5MSIKICAgcGhvdG9zaG9wOkxlZ2FjeUlQVENEaWdlc3Q9IkIzRkE5NzQ4MzA0RkNCRjBGMUI1QTBDQkJEREVDRkMwIgogICBwaG90b3Nob3A6Q29sb3JNb2RlPSIzIgogICBwaG90b3Nob3A6SUNDUHJvZmlsZT0iQWRvYmUgUkdCICgxOTk4KSIKICAgcGhvdG9zaG9wOkF1dGhvcnNQb3NpdGlvbj0iUGhvdG9ncmFwaGVyIgogICBkYzpmb3JtYXQ9ImltYWdlL2pwZWciCiAgIHhtcE1NOkluc3RhbmNlSUQ9InhtcC5paWQ6NjA1MDExOTItMzA1ZC00MmM0LTllNTgtOTI5MjE3NzRjYzZhIgogICB4bXBNTTpEb2N1bWVudElEPSJhZG9iZTpkb2NpZDpwaG90b3Nob3A6YzlmMmVkNjAtZjI0OC0xMzQ1LWIzYzYtNWFjNzgwNWVmZDVhIgogICB4bXBNTTpPcmlnaW5hbERvY3VtZW50SUQ9InhtcC5kaWQ6ZGM3NzkxNTEtZWJmMi00N2M4LWJiYjgtMWY5MjM1ZmE3ODg1Ij4KICAgPHBob3Rvc2hvcDpEb2N1bWVudEFuY2VzdG9ycz4KICAgIDxyZGY6QmFnPgogICAgIDxyZGY6bGk+eG1wLmRpZDpmYjlkNWRiNi0zMDEzLTRmMzMtODA0NS0zM2I2MmQwYjg4ZTQ8L3JkZjpsaT4KICAgIDwvcmRmOkJhZz4KICAgPC9waG90b3Nob3A6RG9jdW1lbnRBbmNlc3RvcnM+CiAgIDxkYzp0aXRsZT4KICAgIDxyZGY6QWx0PgogICAgIDxyZGY6bGkgeG1sOmxhbmc9IngtZGVmYXVsdCI+MDUyMDIyX0NhaXRPcHBlcm1hbm5fSW5zaWRlSUJNX0xvbmRvbl8yNTkyXzAyPC9yZGY6bGk+CiAgICA8L3JkZjpBbHQ+CiAgIDwvZGM6dGl0bGU+CiAgIDxkYzpjcmVhdG9yPgogICAgPHJkZjpTZXE+CiAgICAgPHJkZjpsaT5DYWl0IE9wcGVybWFubjwvcmRmOmxpPgogICAgPC9yZGY6U2VxPgogICA8L2RjOmNyZWF0b3I+CiAgIDxkYzpkZXNjcmlwdGlvbj4KICAgIDxyZGY6QWx0PgogICAgIDxyZGY6bGkgeG1sOmxhbmc9IngtZGVmYXVsdCI+V29tZW4gd29ya2luZyBhdCBvZmZpY2U8L3JkZjpsaT4KICAgIDwvcmRmOkFsdD4KICAgPC9kYzpkZXNjcmlwdGlvbj4KICAgPGRjOnN1YmplY3Q+CiAgICA8cmRmOkJhZz4KICAgICA8cmRmOmxpPkNhaXQgT3BwZXJtYW5uPC9yZGY6bGk+CiAgICAgPHJkZjpsaT5Mb25kb24gR2FyYWdlPC9yZGY6bGk+CiAgICAgPHJkZjpsaT5JbnNpZGUgSUJNPC9yZGY6bGk+CiAgICAgPHJkZjpsaT5Mb25kb248L3JkZjpsaT4KICAgICA8cmRmOmxpPlVLPC9yZGY6bGk+CiAgICAgPHJkZjpsaT5Xb21hbjwvcmRmOmxpPgogICAgIDxyZGY6bGk+QWR1bHQ8L3JkZjpsaT4KICAgICA8cmRmOmxpPlNlbmlvcjwvcmRmOmxpPgogICAgIDxyZGY6bGk+UHJvZmVzc2lvbmFsPC9yZGY6bGk+CiAgICAgPHJkZjpsaT5JbmRpdmlkdWFsPC9yZGY6bGk+CiAgICAgPHJkZjpsaT5JQk0gZW1wbG95ZWU8L3JkZjpsaT4KICAgICA8cmRmOmxpPk9mZmljZTwvcmRmOmxpPgogICAgIDxyZGY6bGk+RGVzazwvcmRmOmxpPgogICAgIDxyZGY6bGk+Q29tcHV0ZXI8L3JkZjpsaT4KICAgICA8cmRmOmxpPldvcmtwbGFjZTwvcmRmOmxpPgogICAgIDxyZGY6bGk+VGVjaG5vbG9neTwvcmRmOmxpPgogICAgIDxyZGY6bGk+QnVzaW5lc3M8L3JkZjpsaT4KICAgICA8cmRmOmxpPkV4cGVydGlzZTwvcmRmOmxpPgogICAgIDxyZGY6bGk+UHJvZ3JhbW1lcjwvcmRmOmxpPgogICAgIDxyZGY6bGk+Q2FuZGlkPC9yZGY6bGk+CiAgICAgPHJkZjpsaT5QbGFubmluZzwvcmRmOmxpPgogICAgPC9yZGY6QmFnPgogICA8L2RjOnN1YmplY3Q+CiAgIDxkYzpyaWdodHM+CiAgICA8cmRmOkFsdD4KICAgICA8cmRmOmxpIHhtbDpsYW5nPSJ4LWRlZmF1bHQiPlJpZ2h0cyBtYW5hZ2VkPC9yZGY6bGk+CiAgICA8L3JkZjpBbHQ+CiAgIDwvZGM6cmlnaHRzPgogICA8eG1wTU06SGlzdG9yeT4KICAgIDxyZGY6U2VxPgogICAgIDxyZGY6bGkKICAgICAgc3RFdnQ6YWN0aW9uPSJzYXZlZCIKICAgICAgc3RFdnQ6aW5zdGFuY2VJRD0ieG1wLmlpZDpkYzc3OTE1MS1lYmYyLTQ3YzgtYmJiOC0xZjkyMzVmYTc4ODUiCiAgICAgIHN0RXZ0OndoZW49IjIwMjItMDctMTdUMTU6MDU6NTgtMDU6MDAiCiAgICAgIHN0RXZ0OnNvZnR3YXJlQWdlbnQ9IkFkb2JlIFBob3Rvc2hvcCAyMy40IChNYWNpbnRvc2gpIgogICAgICBzdEV2dDpjaGFuZ2VkPSIvIi8+CiAgICAgPHJkZjpsaQogICAgICBzdEV2dDphY3Rpb249ImNvbnZlcnRlZCIKICAgICAgc3RFdnQ6cGFyYW1ldGVycz0iZnJvbSBpbWFnZS90aWZmIHRvIGFwcGxpY2F0aW9uL3ZuZC5hZG9iZS5waG90b3Nob3AiLz4KICAgICA8cmRmOmxpCiAgICAgIHN0RXZ0OmFjdGlvbj0iZGVyaXZlZCIKICAgICAgc3RFdnQ6cGFyYW1ldGVycz0iY29udmVydGVkIGZyb20gaW1hZ2UvdGlmZiB0byBhcHBsaWNhdGlvbi92bmQuYWRvYmUucGhvdG9zaG9wIi8+CiAgICAgPHJkZjpsaQogICAgICBzdEV2dDphY3Rpb249InNhdmVkIgogICAgICBzdEV2dDppbnN0YW5jZUlEPSJ4bXAuaWlkOjUyNjQzMWFhLTMwNGItNDQ2Ny1iNjU2LWU4YTVkNzA3N2I5YyIKICAgICAgc3RFdnQ6d2hlbj0iMjAyMi0wNy0xN1QxNTowNTo1OC0wNTowMCIKICAgICAgc3RFdnQ6c29mdHdhcmVBZ2VudD0iQWRvYmUgUGhvdG9zaG9wIDIzLjQgKE1hY2ludG9zaCkiCiAgICAgIHN0RXZ0OmNoYW5nZWQ9Ii8iLz4KICAgICA8cmRmOmxpCiAgICAgIHN0RXZ0OmFjdGlvbj0ic2F2ZWQiCiAgICAgIHN0RXZ0Omluc3RhbmNlSUQ9InhtcC5paWQ6NTNhM2Q3MjctNjc4Zi00YmM1LWJmNmQtMzY5NzhiN2YwOTg4IgogICAgICBzdEV2dDp3aGVuPSIyMDIyLTA3LTE3VDIwOjE0OjE4LTA1OjAwIgogICAgICBzdEV2dDpzb2Z0d2FyZUFnZW50PSJBZG9iZSBQaG90b3Nob3AgMjMuNCAoTWFjaW50b3NoKSIKICAgICAgc3RFdnQ6Y2hhbmdlZD0iLyIvPgogICAgIDxyZGY6bGkKICAgICAgc3RFdnQ6YWN0aW9uPSJzYXZlZCIKICAgICAgc3RFdnQ6aW5zdGFuY2VJRD0ieG1wLmlpZDowY2EwODBjNC05MjcxLTQzMGItYjM1Ni1kZjM3MDBmMTg4MmQiCiAgICAgIHN0RXZ0OndoZW49IjIwMjItMDctMTdUMjA6MjQ6MjQtMDU6MDAiCiAgICAgIHN0RXZ0OnNvZnR3YXJlQWdlbnQ9IkFkb2JlIEJyaWRnZSAyMDIyIChNYWNpbnRvc2gpIgogICAgICBzdEV2dDpjaGFuZ2VkPSIvbWV0YWRhdGEiLz4KICAgICA8cmRmOmxpCiAgICAgIHN0RXZ0OmFjdGlvbj0ic2F2ZWQiCiAgICAgIHN0RXZ0Omluc3RhbmNlSUQ9InhtcC5paWQ6YTVhZTc5NDYtMzVhOS00YzIwLThkMmUtNDlkYWVhOWZmMjhlIgogICAgICBzdEV2dDp3aGVuPSIyMDIyLTA3LTI0VDE2OjU2OjA1LTA1OjAwIgogICAgICBzdEV2dDpzb2Z0d2FyZUFnZW50PSJBZG9iZSBQaG90b3Nob3AgMjMuNCAoTWFjaW50b3NoKSIKICAgICAgc3RFdnQ6Y2hhbmdlZD0iLyIvPgogICAgIDxyZGY6bGkKICAgICAgc3RFdnQ6YWN0aW9uPSJzYXZlZCIKICAgICAgc3RFdnQ6aW5zdGFuY2VJRD0ieG1wLmlpZDpmZTQwNjg1NS1lZTg2LTQ2YTItOTE3ZC0xOTQ0MGRhMWNjN2YiCiAgICA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mitiga\u00e7\u00f5es2\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nAtribui\u00e7\u00e3o\nCom gratid\u00e3o aos patrocinadores executivos do grupo de trabalho \nda \u00e9tica em IA, Christina Montgomery e Francesca Rossi, e \u00e0s \ncontribui\u00e7\u00f5es dos membros do grupo de trabalho Betsy Greytok, Bryan \nBortnick, Catherine Quinlan, David Piorkowski, Eniko Rozsa, Heather \nDomin, Heather Gentile, Jamie VanDodick, Jill Maguire, John McBroom, \nJoshua New, Justin Weisz, Katherine Fick, Kevin Black, Kush Varshney, \nManish Bhide, Manish Goyal, Melis Kiziltay, Michael Epstein, Michael \nHind, Milena Pribic, Phaedra Boinodiris, Rogerio Abreu de Paula, \nSaishruthi Swaminathan e Suj Perepa.3\n\u00cdndice\n04\n \nExecutivo \nResumo\n05\n \nIntrodu\u00e7\u00e3o\n06\n \nBenef\u00edcios dos  \nmodelos de base\n08\n \nRiscos dos  \nmodelos de base\n16\n \nRisco \nExemplos\n24\n \nPrinc\u00edpios, pilares \ne governan\u00e7a\n25\n \nProte\u00e7\u00f5es \ne mitiga\u00e7\u00f5es\n27\n \nPol\u00edticas, regulamenta\u00e7\u00f5es \ne\u00a0melhores pr\u00e1ticas de IA \nExemplos4\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nResumo executivo\nA ascens\u00e3o dos modelos de base oferece \u00e0s empresas novas e \nempolgantes possibilidades, mas tamb\u00e9m levanta quest\u00f5es novas e \namplas sobre design, desenvolvimento, implementa\u00e7\u00e3o e uso \u00e9tico. \nSegundo uma recente pesquisa sobre IA generativa do IBM Institute for \nBusiness Value, as organiza\u00e7\u00f5es j\u00e1 est\u00e3o manifestando preocupa\u00e7\u00f5es \nsobre quest\u00f5es relacionadas \u00e0 confian\u00e7a, especificamente como \nbarreiras para investimentos. Suas principais preocupa\u00e7\u00f5es s\u00e3o \nciberseguran\u00e7a (57%), privacidade (51%) e precis\u00e3o (47%). Muitas \norganiza\u00e7\u00f5es estavam levando essas preocupa\u00e7\u00f5es a s\u00e9rio antes \nda \u2018consumeriza\u00e7\u00e3o\u2019 da IA generativa, expressando sua inten\u00e7\u00e3o de \ninvestir pelo menos 40% mais em \u00e9tica de IA nos pr\u00f3ximos tr\u00eas anos. \nA\u00a0conscientiza\u00e7\u00e3o sobre riscos e poss\u00edveis maneiras de mitig\u00e1-los \u00e9 o  \nprimeiro passo crucial para a cria\u00e7\u00e3o de sistemas de IA confi\u00e1veis.\nNeste documento:\nExploraremos as vantagens dos modelos de base, incluindo \nsua capacidade de realizar tarefas desafiadoras, potencial \npara acelerar a ado\u00e7\u00e3o de IA, habilidade de aumentar \na produtividade e os benef\u00edcios econ\u00f4micos que eles \nproporcionam. \nDiscutiremos as tr\u00eas categorias de risco, incluindo riscos \nconhecidos de formas anteriores de IA, riscos conhecidos \namplificados por modelos de base e riscos emergentes \nintr\u00ednsecos aos recursos generativos dos modelos de base. \nAbordaremos os princ\u00edpios, os pilares e o controle que \nformam a base das iniciativas \u00e9ticas de IA da IBM e \nsugeriremos barreiras para a mitiga\u00e7\u00e3o de riscos.5\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nIntrodu\u00e7\u00e3o\n\u00c0 medida que o uso de IA continua se expandindo, os grandes e \ncomplexos modelos de IA est\u00e3o fornecendo resultados promissores \nde desempenho, bem como resolvendo alguns dos problemas mais \ndesafiadores da sociedade. No entanto, criar grandes conjuntos de dados \nde treinamento e modelos complexos para cada aplicativo de IA pode \nser extremamente dif\u00edcil para as empresas. Modelos de base fornecem \num caminho para alcan\u00e7ar o melhor dos dois mundos: desenvolver \nmodelos de \u00faltima gera\u00e7\u00e3o poderosos e reutiliz\u00e1-los diretamente ou \naplicar m\u00e9todos de ajuste para implementar uma variedade de casos \nde uso, em vez de treinar novos modelos para cada caso de uso. Por \nexemplo, a IBM Research desenvolveu modelos de base para inspe\u00e7\u00e3o \nvisual. Esses modelos de base aprendem a representa\u00e7\u00e3o geral de \nsuperf\u00edcies e corredores de concreto e podem ser ajustados ainda \nmais para casos de uso espec\u00edficos, como detec\u00e7\u00e3o de rachaduras ou \ninspe\u00e7\u00e3o de defeitos com dados menos rotulados.\nA IBM define um modelo de base como um modelo de IA que pode \nser adaptado a uma ampla gama de tarefas de recebimento de dados. \nOs\u00a0modelos de base normalmente s\u00e3o modelos generativos de grande \nescala treinados em dados n\u00e3o rotulados usando autossupervis\u00e3o. \nComo\u00a0modelos de grande escala, os modelos de base podem incluir \nbilh\u00f5es de par\u00e2metros.\nA IBM \u00e9 uma empresa de nuvem h\u00edbrida e IA com vasta reputa\u00e7\u00e3o como \nadministradora de dados respons\u00e1vel e comprometida com a \u00e9tica em \nIA. Usando a capacidade de nossas equipes de pesquisa , produto e \nconsultoria , juntamente com parceiros externos, como a Hugging Face, \najudamos a trazer o poder dos modelos de base para nossos clientes \ne a criar IAs confi\u00e1veis em qualquer empresa. A IBM tamb\u00e9m continua \ninvestindo na cria\u00e7\u00e3o de novas plataformas, como a IA IBM watsonx \ne plataformas e tecnologias de dados, para projetar e desenvolver \nmodelos de IA para se comportar de maneira audit\u00e1vel e confi\u00e1vel. \nEste documento descreve o ponto de vista da IBM sobre a \u00e9tica dos \nmodelos de base. \u00c9 a primeira vers\u00e3o, e as vers\u00f5es futuras expandir\u00e3o \nv\u00e1rios aspectos da abordagem \u00e9tica do modelo de base da IBM. \nEsperamos que este documento seja \u00fatil para todos os stakeholders no \ndesenvolvimento, implementa\u00e7\u00e3o e uso do modelo de base de forma \nrespons\u00e1vel.6\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nBenef\u00edcios dos  \nmodelos de base\nOs modelos de base podem melhorar significativamente o processo de \ndesenvolvimento de sistemas de IA e auxiliar no avan\u00e7o da IA da fase de \nexplora\u00e7\u00e3o para a ado\u00e7\u00e3o nas empresas. Seus benef\u00edcios incluem:\nRealizar tarefas complexas \nModelos de base mostram um aumento significativo no desempenho \nna resolu\u00e7\u00e3o de problemas complexos e dif\u00edceis. Por exemplo, \no modelo de base geoespacial da colabora\u00e7\u00e3o IBM e NASA foi \nprojetado para converter os dados de sat\u00e9lite da NASA em mapas \nde desastres naturais, como inunda\u00e7\u00f5es e outras mudan\u00e7as de \ncen\u00e1rio. O modelo tamb\u00e9m pode ser usado para ajudar a revelar \no\u00a0passado do nosso planeta; estimar riscos para culturas, empresas \nou infraestruturas devido ao clima severo; desenvolver estrat\u00e9gias \npara se adaptar \u00e0s mudan\u00e7as clim\u00e1ticas; e auxiliar no agroneg\u00f3cio. \nO modelo est\u00e1 planejado para ser disponibilizado previamente aos \nclientes IBM por meio do IBM Environmental Intelligence Suite.\nPara ilustrar, o MoLFormer-XL da IBM \u00e9 um modelo de base que \n\u00e9\u00a0capaz de inferir a estrutura de mol\u00e9culas a partir de representa\u00e7\u00f5es \nsimples, tornando mais f\u00e1cil a aprendizagem de v\u00e1rias tarefas \nde recebimento de dados, como prever as propriedades f\u00edsicas \ne\u00a0qu\u00e2nticas de uma mol\u00e9cula, identificar mol\u00e9culas semelhantes, \nrastrear mol\u00e9culas j\u00e1 aprovadas para novos casos de uso e descobrir \nnovas mol\u00e9culas. Moderna e IBM est\u00e3o explorando formas de \nusar o MoLExer para ajudar a prever propriedades das mol\u00e9culas e \nentender as caracter\u00edsticas de poss\u00edveis medicamentos de mRNA.\nMaior produtividade \nA natureza generativa dos modelos de base amplia o n\u00famero de \n\u00e1reas em que a IA pode ser usada em uma empresa para ajudar a \nmelhorar a\u00a0produtividade, automatizando tarefas rotineiras e tediosas \ne\u00a0permitindo que os usu\u00e1rios dediquem mais tempo ao trabalho \ncriativo e inovador. Por exemplo, o IBM Watsonx Code Assistant, \ndesenvolvido com modelos de base, possibilita que desenvolvedores, \nindependentemente do n\u00edvel de experi\u00eancia, escrevam c\u00f3digos usando \nrecomenda\u00e7\u00f5es geradas por IA.\nTime to value mais r\u00e1pido \nModelos de base geralmente s\u00e3o treinados com dados n\u00e3o rotulados, \nque est\u00e3o mais dispon\u00edveis em grandes quantidades do que dados \nrotulados. Uma vez treinados, os modelos de base podem ser usados \ndiretamente ou ap\u00f3s serem ajustados para aplicativos de recebimento \nde dados, usando uma pequena quantidade de dados rotulados  \nespecializados, que podem diminuir a cria\u00e7\u00e3o do time to value.7\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nUtilize diversas modalidades de dados \nOs modelos de base podem ser treinados usando diversas modalidades \nde dados, como l\u00edngua natural, texto, imagem e \u00e1udio. Eles tamb\u00e9m \npodem ser aplicados a tarefas que exigem diferentes tipos de dados, \ncomo dados de s\u00e9ries temporais, dados geoespaciais, dados tabulares, \ndados semiestruturados e dados de modalidade mista, como texto \ncombinado com imagens.\nDespesas amortizadas \nEmbora o custo inicial do treinamento de um modelo de base seja \nsignificativamente maior do que o treinamento de um modelo de IA \ntradicional, o custo adicional para aplic\u00e1-lo em uma nova tarefa \u00e9 \nconsideravelmente menor. O uso de modelos de base pr\u00e9-treinados  \npoderia ajudar a eliminar a necessidade de que as empresas fa\u00e7am \ninvestimentos substanciais para treinar modelos de base e explorar suas \nnovas capacidades. Para uma empresa, a confiabilidade dos modelos, \na\u00a0efici\u00eancia energ\u00e9tica, o desempenho, a portabilidade e a capacidade \nde\u00a0usar dados corporativos de forma eficaz e segura s\u00e3o fundamentais.\nA IBM permite que as empresas \ncriem e detenham o valor de \nmodelos de base para seus \nneg\u00f3cios, trazendo as melhores \ninova\u00e7\u00f5es da comunidade de \nIA aberta e global, operando de \nforma eficiente em ambientes de \ncomputa\u00e7\u00e3o h\u00edbrida, ajudando \na mitigar riscos e controlando \nrigorosamente a IA.8\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nRiscos dos  \nmodelos de base\nComo todas as tecnologias que avan\u00e7am rapidamente, os modelos \nde base oferecem riscos e benef\u00edcios. Alguns s\u00e3o riscos legais, \ncomo restri\u00e7\u00f5es \u00e0 movimenta\u00e7\u00e3o ou uso de dados, e precisam \nser cuidadosamente avaliados de acordo com a legisla\u00e7\u00e3o atual e \nem evolu\u00e7\u00e3o. Outros riscos t\u00eam uma natureza \u00e9tica e devem ser \nconsiderados cuidadosamente para que a tecnologia tenha um impacto \npositivo. Em geral, os riscos de IA levantam quest\u00f5es sociot\u00e9cnicas \ne\u00a0devem ser abordados e mitigados por meio de m\u00e9todos sociot\u00e9cnicos, \nincluindo ferramentas de software, processos de avalia\u00e7\u00e3o de risco, \nframeworks de \u00e9tica em IA, mecanismos de controle, consultas \nmultistakeholder, padr\u00f5es e regulamenta\u00e7\u00e3o. Iremos listar os riscos \nconsiderando as seguintes 3 categorias:\n1. Tradicional. Riscos conhecidos de formas anteriores ou anteriores \nde\u00a0sistemas de IA\n2. Amplificados. Riscos conhecidos, mas agora intensificados devido \u00e0s \ncaracter\u00edsticas intr\u00ednsecas dos modelos de base, principalmente seus \nrecursos generativos inerentes\n3. Novo. Riscos emergentes intr\u00ednsecos aos modelos de base e suas \ncapacidades generativas inerentes\n \nTamb\u00e9m estruturamos a lista de riscos em rela\u00e7\u00e3o a se est\u00e3o \nprincipalmente associados ao conte\u00fado fornecido ao modelo \nbase, o\u00a0input, ou ao conte\u00fado gerado por ele, o output, ou se est\u00e3o \nrelacionados a desafios adicionais.9\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n1. Riscos associados \u00e0 entrada\nGrupo Risco Indicador\nFase de treinamento e ajuste\nJusti\u00e7a Vi\u00e9s de dados: vi\u00e9s hist\u00f3rico, representacional \ne social presente nos dados usados para \ntreinar e fazer o ajuste fino do modelo.\nAmplificadoTreinar um sistema de IA com dados enviesados, como vi\u00e9s hist\u00f3rico ou \nrepresentacional, pode resultar em outputs enviesados ou distorcidos \nque podem representar injustamente ou discriminar certos grupos \nou indiv\u00edduos. Al\u00e9m dos impactos negativos na sociedade, entidades \ncomerciais podem enfrentar consequ\u00eancias legais, interrup\u00e7\u00e3o \ndas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o decorrentes dos resultados \nenviesados do modelo.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nEnvenenamento de dados: um tipo de ataque \nadversarial no qual um advers\u00e1rio ou agente \ninterno malicioso injeta intencionalmente \namostras corrompidas, falsas, enganosas \nou incorretas no conjunto de dados de \ntreinamento ou ajuste fino.\nTradicionalO envenenamento de dados pode tornar o modelo sens\u00edvel a um padr\u00e3o \nde dados malicioso e produzir o output desejado pelo advers\u00e1rio. Isso \npode criar um risco de seguran\u00e7a onde advers\u00e1rios podem manipular \no\u00a0comportamento do modelo em seu pr\u00f3prio benef\u00edcio.  Al\u00e9m de produzir \nresultados n\u00e3o intencionais e potencialmente maliciosos, uma diverg\u00eancia \ndo modelo causada por envenenamento de dados pode resultar em \nentidades comerciais enfrentando consequ\u00eancias legais, interrup\u00e7\u00e3o \ndas\u00a0opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\nRobustez\nCuradoria de dados: quando os dados de \ntreinamento ou ajuste s\u00e3o coletados ou \npreparados de forma inadequada.\nAmplificadoUma curadoria de dados inadequada pode afetar adversamente como \num modelo \u00e9 treinado, resultando em um modelo que n\u00e3o se comporta \nde acordo com os valores pretendidos. Exemplos de uma curadoria de \ndados inadequada podem incluir erros de rotulagem ou anota\u00e7\u00e3o nos \ndados usados para treinar ou ajustar o modelo. Corrigir problemas ap\u00f3s \no treinamento e a implementa\u00e7\u00e3o do modelo pode ser insuficiente para \ngarantir um comportamento adequado. Um comportamento inadequado do \nmodelo pode resultar em entidades comerciais enfrentando consequ\u00eancias \nlegais, interrup\u00e7\u00f5es nas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\nAlinhamento \nde valor\nRetreinamento baseado em downstream: \nusando de outputs indesej\u00e1veis (imprecisos, \ninadequados, conte\u00fado do usu\u00e1rio, etc.) \nde aplica\u00e7\u00f5es downstream para fins de \nretreinamento.\nNovoO reaproveitamento de output downstream para treinar novamente um \nmodelo sem implementar a verifica\u00e7\u00e3o humana adequada aumenta as \nchances de que outputs indesej\u00e1veis sejam incorporados aos dados de \ntreinamento ou ajuste do modelo, possivelmente gerando outputs ainda \nmais indesej\u00e1veis.  Comportamento inadequado do modelo pode resultar \nem entidades empresariais enfrentando consequ\u00eancias legais ou danos \n\u00e0 reputa\u00e7\u00e3o.  N\u00e3o cumprir com as leis de transfer\u00eancia de dados pode \nresultar em multas e outras consequ\u00eancias legais.\nTransfer\u00eancia de dados: leis e outras \nrestri\u00e7\u00f5es podem limitar ou proibir a \ntransfer\u00eancia de dados.\nTradicionalRestri\u00e7\u00f5es \u00e0 transfer\u00eancia de dados podem afetar a disponibilidade dos \ndados necess\u00e1rios para treinar um modelo de IA e podem resultar em \ndados mal representados. Al\u00e9m do impacto na disponibilidade de dados, \no n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de transfer\u00eancia de dados \npode resultar em multas e outras consequ\u00eancias legais. \nLeis de dados\nUso de dados: leis e outras restri\u00e7\u00f5es podem \nlimitar ou proibir o uso de alguns dados para \ncasos de uso espec\u00edficos de IA.\nTradicionalO n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de uso de dados pode \nresultar em multas e outras consequ\u00eancias legais. \nAquisi\u00e7\u00e3o de dados: leis e outras \nregulamenta\u00e7\u00f5es podem limitar a coleta \nde certos tipos de dados para casos de uso \nespec\u00edficos de IA.\nAmplificadoO n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es da aquisi\u00e7\u00e3o de dados \npode resultar em multas e outras consequ\u00eancias legais. 10\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nGrupo Risco Indicador\nPropriedade \nintelectual\nDireitos de uso de dados: termos de servi\u00e7o, \nleis de direitos autorais, conformidade com \nlicen\u00e7as ou outras quest\u00f5es de propriedade \nintelectual podem restringir a capacidade \nde usar certos dados para a constru\u00e7\u00e3o \nde\u00a0modelos. \nAmplificadoAs leis e regulamenta\u00e7\u00f5es referentes ao uso de dados para treinar IA \ns\u00e3o inst\u00e1veis e podem variar de pa\u00eds para pa\u00eds, o que cria desafios no \ndesenvolvimento de modelos. Se o uso de dados violar regras ou restri\u00e7\u00f5es, \nas entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nTranspar\u00eancia de dados: desafio em \ndocumentar como os dados de um modelo \nforam coletados, curados e utilizados \npara\u00a0trein\u00e1-lo.\nAmplificadoA transpar\u00eancia dos dados \u00e9 importante para a conformidade legal e \u00e9tica \nda IA. A falta de informa\u00e7\u00f5es limita a capacidade de avaliar os riscos \nassociados aos dados. A falta de requisitos padronizados pode limitar a \ndivulga\u00e7\u00e3o, pois as organiza\u00e7\u00f5es protegem segredos comerciais e tentam \nevitar que outros copiem seus modelos.\nTranspar\u00eancia\nProced\u00eancia dos dados: desafio em \npadronizar e estabelecer m\u00e9todos para \nverificar de onde os dados vieram.\nAmplificadoNem todas as fontes de dados s\u00e3o confi\u00e1veis. Os dados podem ter sido \ncoletados, manipulados ou falsificados de forma anti\u00e9tica. O uso de dados \nn\u00e3o confi\u00e1veis pode resultar em comportamentos indesej\u00e1veis no modelo. \nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nInforma\u00e7\u00f5es pessoais nos dados: inclus\u00e3o \nou presen\u00e7a de informa\u00e7\u00f5es pessoalmente \nidentific\u00e1veis (PII) e informa\u00e7\u00f5es pessoais \nsens\u00edveis (SPI) nos dados usados para treinar \nou ajustar o modelo.\nTradicionalSe n\u00e3o desenvolvido adequadamente para proteger dados sens\u00edveis, \no\u00a0modelo pode expor informa\u00e7\u00f5es pessoais no output gerado. Al\u00e9m disso, \ndados pessoais ou sens\u00edveis devem ser revisados e tratados de acordo \ncom as leis e regulamenta\u00e7\u00f5es de privacidade. As entidades empresariais \npodem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es \ne\u00a0outras consequ\u00eancias legais se forem encontradas em viola\u00e7\u00e3o.\nPrivacidade\nReidentifica\u00e7\u00e3o: mesmo com a remo\u00e7\u00e3o de \ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis \n(PII) e informa\u00e7\u00f5es pessoais sens\u00edveis (SPI) \ndos dados, ainda pode ser poss\u00edvel identificar \npessoas devido a outros recursos dispon\u00edveis \nnos dados. \nTradicionalOs dados que podem revelar informa\u00e7\u00f5es pessoais ou sens\u00edveis devem \nser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es de privacidade, pois \nas entidades comerciais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se forem \nconsideradas em viola\u00e7\u00e3o.\nDireitos de privacidade de dados: desafios \nrelacionados \u00e0 capacidade de fornecer \ndireitos do titular dos dados, como op\u00e7\u00e3o \nde exclus\u00e3o, direito de acesso e direito ao \nesquecimento.\nAmplificadoA identifica\u00e7\u00e3o ou uso inadequado de dados pode resultar em viola\u00e7\u00e3o das \nleis de privacidade. O uso inadequado ou um pedido de remo\u00e7\u00e3o de dados \npoderia obrigar as organiza\u00e7\u00f5es a reconfigurar o modelo, o que \u00e9 caro. \nAl\u00e9m disso, as entidades empresariais podem enfrentar multas, danos \u00e0 \nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se n\u00e3o \ncumprirem as regras e regulamenta\u00e7\u00f5es de privacidade de dados.\nConsentimento informado: dados \ncoletados para treinar modelos de IA sem \no consentimento informado do propriet\u00e1rio, \nmesmo quando legalmente permitido.\nTradicionalEm algumas circunst\u00e2ncias, pode ser anti\u00e9tico coletar e usar dados \nsem o\u00a0consentimento da pessoa. Existem tamb\u00e9m poss\u00edveis riscos \nreputacionais associados a esse tipo de uso.11\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nGrupo Risco Indicador\nInfer\u00eancia Fase\nPrivacidade Informa\u00e7\u00f5es pessoais no prompt: divulgar \ninforma\u00e7\u00f5es pessoais ou informa\u00e7\u00f5es \npessoais sens\u00edveis como parte do prompt \nsolicita\u00e7\u00e3o enviada ao modelo.\nNovoOs dados do prompt podem ser armazenados ou posteriormente utilizados \npara outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos \nde dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es \nde privacidade. Sem um armazenamento e uso adequados dos dados, \nas entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nInforma\u00e7\u00f5es de IP no prompt: divulga\u00e7\u00e3o de \ninforma\u00e7\u00f5es de direitos autorais ou outras \ninforma\u00e7\u00f5es de propriedade intelectual como \nparte do prompt enviado ao modelo.\nNovoOs dados do prompt podem ser armazenados ou posteriormente utilizados \npara outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos \nde dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es \nde propriedade intelectual. Sem um armazenamento e uso adequados \ndos dados, as entidades empresariais podem enfrentar multas, danos \n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPropriedade \nintelectual\nDados confidenciais no prompt: inclus\u00e3o de \ndados confidenciais como parte do prompt \nenviado ao modelo.\nNovoSe n\u00e3o for desenvolvido adequadamente para proteger dados confidenciais, \no modelo pode expor informa\u00e7\u00f5es confidenciais ou propriedade intelectual \nno output gerado. Al\u00e9m disso, informa\u00e7\u00f5es confidenciais dos usu\u00e1rios finais \npodem ser coletadas e armazenadas inadvertidamente.\nRobustez Ataque de evas\u00e3o: tentativa de fazer com \nque um modelo produza outputs incorretos \nperturbando os dados enviados ao modelo \ntreinado.\nAmplificadoOs ataques de evas\u00e3o alteram o comportamento do modelo, geralmente \npara beneficiar o atacante. Se os resultados de output n\u00e3o forem \ndevidamente considerados, as entidades empresariais podem enfrentar \nmultas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \nconsequ\u00eancias legais.\nAtaques baseados em prompt: ataques \nadversos, como inje\u00e7\u00e3o de prompt (tentativa \nde for\u00e7ar um modelo a produzir um output \ninesperado), vazamento de prompt (tentativas \nde extrair o prompt do sistema de um \nmodelo), desbloqueio (tentativas de romper \nas prote\u00e7\u00f5es estabelecidas no modelo), \ne\u00a0prepara\u00e7\u00e3o de prompt (tentativa de for\u00e7ar \num modelo a produzir um output alinhado \nao\u00a0prompt).\nNovoDependendo do conte\u00fado revelado, as entidades empresariais podem \nenfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \nconsequ\u00eancias legais.12\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n2. Riscos associados \u00e0 sa\u00edda\nGrupo Risco Indicador\nJusti\u00e7a Vi\u00e9s de output: o conte\u00fado gerado pode \nrepresentar injustamente certos grupos ou \nindiv\u00edduos.\nNovoO vi\u00e9s pode prejudicar os usu\u00e1rios dos modelos de IA e amplificar \ncomportamentos discriminat\u00f3rios existentes. As entidades empresariais \npodem enfrentar danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \nconsequ\u00eancias.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nVi\u00e9s de decis\u00e3o: quando um grupo \u00e9 \ninjustamente favorecido em rela\u00e7\u00e3o a outro \ndevido aos efeitos das decis\u00f5es tomadas por \nhumanos usando o output do modelo.\nTradicionalO vi\u00e9s pode prejudicar as pessoas afetadas pelas decis\u00f5es do modelo. \nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nViola\u00e7\u00e3o de direitos autorais: quando \num modelo gera conte\u00fado que \u00e9 muito \nsemelhante ou id\u00eantico a uma obra existente \nprotegida por direitos autorais ou abrangida \npor um acordo de licen\u00e7a de c\u00f3digo aberto.\nNovoAs leis e regulamenta\u00e7\u00f5es referentes ao uso de conte\u00fado que se assemelha \nou \u00e9 muito semelhante a outros dados protegidos por direitos autorais s\u00e3o \namplamente indefinidos e podem variar de pa\u00eds para pa\u00eds, o que representa \ndesafios na determina\u00e7\u00e3o e implementa\u00e7\u00e3o da conformidade. As entidades \nempresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das \nopera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPropriedade \nintelectual\nAlucina\u00e7\u00e3o: gera\u00e7\u00e3o de conte\u00fado \nfactualmente impreciso ou n\u00e3o verdadeiro.\nNovoOutputs falsos podem induzir os usu\u00e1rios ao erro e serem incorporados \nem artefatos posteriores, propagando ainda mais a desinforma\u00e7\u00e3o. Isso \npode prejudicar tanto os propriet\u00e1rios quanto os usu\u00e1rios dos modelos de \nIA. Tamb\u00e9m, as entidades empresariais podem enfrentar multas, danos \n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nOutputs t\u00f3xicos: quando o modelo produz \nconte\u00fado odioso, abusivo e profano (HAP) ou \nobsceno.\nNovoConte\u00fado odioso, abusivo e profano (HAP) ou obsceno pode impactar \nadversamente e prejudicar as pessoas que interagem com o modelo. \nTamb\u00e9m, as entidades empresariais podem enfrentar multas, danos \u00e0 \nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nAlinhamento de \nvalor\nConselhos perigosos: quando um modelo \nfornece conselhos sem ter informa\u00e7\u00f5es \nsuficientes, resultando em poss\u00edveis perigos \nse o conselho for seguido.\nNovoUma pessoa pode agir com base em conselhos incompletos ou \npreocupar-se com uma situa\u00e7\u00e3o que n\u00e3o se aplica a ela devido \u00e0 natureza \nsupergeneralizada do conte\u00fado gerado.\nDissemina\u00e7\u00e3o de desinforma\u00e7\u00e3o: utiliza\u00e7\u00e3o \nde um modelo para criar informa\u00e7\u00f5es \nenganosas ou falsas com o intuito de enganar \nou influenciar um p\u00fablico-alvo.\nNovoEspalhar desinforma\u00e7\u00e3o pode afetar a capacidade de uma pessoa \nde tomar decis\u00f5es informadas. As entidades empresariais podem \nenfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es \ne\u00a0outras consequ\u00eancias\u00a0legais.\nToxicidade: utilizar um modelo para gerar \nconte\u00fado odioso, abusivo e profano (HAP) \nou\u00a0obsceno.\nNovoConte\u00fado t\u00f3xico pode ter um impacto negativo no bem-estar de seus \ndestinat\u00e1rios. As entidades empresariais podem enfrentar multas, danos \n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nUso indevido \nUso n\u00e3o consensual: utilizar um modelo para \nimitar pessoas por meio de v\u00eddeo (deepfakes), \nimagens, \u00e1udio ou outras modalidades sem \no\u00a0consentimento delas.\nAmplificadoDeepfakes podem disseminar desinforma\u00e7\u00e3o sobre uma pessoa, \npossivelmente resultando em impactos negativos na reputa\u00e7\u00e3o da pessoa. \nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.Expor informa\u00e7\u00f5es pessoais: quando \ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis (PII) \nou informa\u00e7\u00f5es pessoais sens\u00edveis (SPI) s\u00e3o \nutilizadas nos dados de treinamento, dados \nde ajuste fino ou como parte do prompt, \nos modelos podem revelar esses dados no \noutput gerado.\nNovoCompartilhar informa\u00e7\u00f5es pessoalmente identific\u00e1veis das pessoas afeta \nseus direitos e as torna mais vulner\u00e1veis. Al\u00e9m disso, os dados dos outputs \ndevem ser revisados em conformidade com as leis e regulamenta\u00e7\u00f5es de \nprivacidade, pois as entidades comerciais podem enfrentar multas, danos \n\u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se \nforem encontradas em viola\u00e7\u00e3o das leis ou regulamenta\u00e7\u00f5es de\u00a0privacidade \nou uso de dados. \nPrivacidade\nOutput inexplic\u00e1vel: desafios em explicar por \nque o output do modelo foi gerado.\nAmplificado Os modelos de base s\u00e3o baseados em arquiteturas complexas de \ndeep learning, tornando as explica\u00e7\u00f5es para seus outputs dif\u00edceis. \nSem\u00a0explica\u00e7\u00f5es claras para o output do modelo, \u00e9 dif\u00edcil para os usu\u00e1rios, \nvalidadores do modelo e auditores entenderem e confiarem no modelo. \nA\u00a0falta de transpar\u00eancia pode acarretar consequ\u00eancias legais em dom\u00ednios \naltamente regulamentados. Explica\u00e7\u00f5es equivocadas podem levar a uma \nconfian\u00e7a excessiva.\nExplicabilidade\nAtribui\u00e7\u00e3o n\u00e3o confi\u00e1vel de fontes: \ndesafios em determinar de quais dados de \ntreinamento ou ajuste fino o modelo gerou \numa parte ou todo o seu output.\nNovoA incapacidade de rastrear a origem ou proced\u00eancia da sa\u00edda torna \ndif\u00edcil para os usu\u00e1rios, validadores de modelo e auditores entenderem \ne\u00a0confiarem no modelo.\nRastreabilidade\nExcesso/falta de confian\u00e7a: quando uma \npessoa deposita confian\u00e7a em excesso ou em \nfalta na orienta\u00e7\u00e3o de um modelo de IA.\nAmplificadoEm tarefas onde os humanos baseiam suas escolhas em sugest\u00f5es da IA, \numa confian\u00e7a excessiva ou insuficiente pode levar a decis\u00f5es inadequadas \ndevido \u00e0 confian\u00e7a equivocada no sistema de IA, com consequ\u00eancias \nnegativas que aumentam com a import\u00e2ncia da decis\u00e3o. Decis\u00f5es ruins \npodem prejudicar as pessoas e podem resultar em preju\u00edzos financeiros, \ndanos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias \nlegais para as entidades comerciais.\nConfian\u00e7a \nequivocada\n13\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nGrupo Risco IndicadorPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nUso perigoso: utilizar um modelo com a \u00fanica \ninten\u00e7\u00e3o de prejudicar pessoas.\nNovoAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nUso inadequado: utilizar um modelo para um \nfim para o qual o modelo n\u00e3o foi projetado.\nAmplificadoReutilizar um modelo sem compreender seus dados originais, inten\u00e7\u00e3o \nde design e objetivos pode resultar em comportamentos inesperados \ne\u00a0indesejados do modelo.\nGera\u00e7\u00e3o de c\u00f3digo prejudicial: modelos \npodem gerar c\u00f3digo que, quando executado, \ncausa danos ou afeta inadvertidamente \noutros sistemas.\nNovoA execu\u00e7\u00e3o de c\u00f3digo prejudicial pode abrir vulnerabilidades nos sistemas \nde TI. As entidades empresariais podem enfrentar multas, danos \u00e0 \nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nGera\u00e7\u00e3o \nde c\u00f3digo \nprejudicial\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o revelar que o conte\u00fado \n\u00e9\u00a0gerado por um modelo de IA.\nNovoA omiss\u00e3o do conte\u00fado produzido por IA pode ser interpretada como \nenganosa, levando a uma diminui\u00e7\u00e3o da confian\u00e7a. A inten\u00e7\u00e3o de enganar \npode resultar na redu\u00e7\u00e3o da capacidade de a\u00e7\u00e3o humana, em multas, \ndanos \u00e0 reputa\u00e7\u00e3o e outras consequ\u00eancias legais.14\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n3. Desafios\nGrupo Risco Indicador\nControle Transpar\u00eancia do Modelo: a falta de \ntranspar\u00eancia do modelo ou documenta\u00e7\u00e3o \ninsuficiente do processo de desenvolvimento \ndo modelo dificulta a compreens\u00e3o de como \ne por que um modelo foi constru\u00eddo e quem o \nconstruiu, aumentando assim a possibilidade \nde uso n\u00e3o intencional do modelo.\nTradicionalA transpar\u00eancia \u00e9 importante para conformidade legal, \u00e9tica em IA e \norienta\u00e7\u00e3o para o uso apropriado de modelos. A falta de informa\u00e7\u00f5es \npode tornar mais dif\u00edcil avaliar os riscos, alterar o modelo ou reutiliz\u00e1-lo. \nO conhecimento sobre quem construiu um modelo tamb\u00e9m pode ser um \nfator importante na decis\u00e3o de confiar nele.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nResponsabilidade: o processo de \ndesenvolvimento de modelos de base \u00e9 \ncomplexo, com muitos dados, processos \ne pap\u00e9is envolvidos. Quando o output do \nmodelo n\u00e3o funciona conforme o esperado, \npode ser dif\u00edcil determinar a causa raiz e \natribuir responsabilidade. \nAmplificadoSem documentar adequadamente decis\u00f5es e atribuir responsabilidades, \npode n\u00e3o ser poss\u00edvel determinar a responsabilidade por comportamentos \ninesperados ou uso indevido.\nResponsabilidade legal: Determinar quem \n\u00e9\u00a0respons\u00e1vel pelo modelo de base.\nNovoSe a propriedade ou responsabilidade pelo desenvolvimento do modelo for \nincerta, reguladores e outras partes interessadas podem ter preocupa\u00e7\u00f5es \nem rela\u00e7\u00e3o ao modelo, porque n\u00e3o ficar\u00e1 claro quem \u00e9, ou deveria ser, \nrespons\u00e1vel por problemas com ele ou pode responder a perguntas sobre \nele. Usu\u00e1rios de modelos sem propriedade clara podem enfrentar desafios \npara cumprir futuras regulamenta\u00e7\u00f5es de IA.\nConformidade \nlegal\nPropriedade do Conte\u00fado Gerado: determinar \na propriedade do conte\u00fado gerado por IA.\nNovoAs leis e regulamenta\u00e7\u00f5es relacionadas \u00e0 propriedade do conte\u00fado gerado \npor IA est\u00e3o em grande parte indefinidas e podem variar de pa\u00eds para \npa\u00eds. Entidades empresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPropriedade Intelectual do Conte\u00fado \nGerado: incerteza legal sobre os direitos \nde propriedade intelectual relacionados ao \nconte\u00fado gerado.\nNovoAs leis e regulamenta\u00e7\u00f5es sobre a determina\u00e7\u00e3o da possibilidade de \ndireitos autorais e da patenteabilidade do conte\u00fado gerado por IA est\u00e3o \nem grande parte indefinidas e podem variar de pa\u00eds para pa\u00eds. Entidades \nempresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das \nopera\u00e7\u00f5es e outras consequ\u00eancias legais se o conte\u00fado gerado estiver \nprotegido por direitos de propriedade intelectual.\nAtribui\u00e7\u00e3o da Fonte: determinar a \nproced\u00eancia do conte\u00fado gerado.\nAmplificadoSe o modelo gera um output que \u00e9 id\u00eantico aos dados usados para \ntreinar o modelo, ele deve fornecer a proveni\u00eancia desse output. A falha \nem fazer isso pode colocar as entidades comerciais que implementam \nou usam o modelo em risco legal.\nImpacto nos Empregos: a ado\u00e7\u00e3o \ngeneralizada de sistemas de IA baseados em \nmodelos fundamentais pode levar \u00e0 perda \nde empregos das pessoas, \u00e0 medida que seu \ntrabalho \u00e9 automatizado, se elas n\u00e3o forem \ncapacitadas para novas habilidades. \nAmplificadoA perda de empregos pode levar a uma redu\u00e7\u00e3o de renda e, portanto, \npode ter um impacto negativo na sociedade e no bem-estar humano. \nO ressurgimento pode ser desafiador dada a velocidade da evolu\u00e7\u00e3o \ntecnol\u00f3gica. \nSocial \nImpactoExplora\u00e7\u00e3o Humana: uso de trabalho \nfantasma (ghost work) na forma\u00e7\u00e3o de \nmodelos de IA, condi\u00e7\u00f5es de trabalho \ninadequadas, falta de cuidados de sa\u00fade, \nincluindo sa\u00fade mental, compensa\u00e7\u00e3o \ninjusta.\nAmplificadoOs modelos de base ainda dependem do trabalho humano para obter, \ngerenciar e engenhar os dados que s\u00e3o usados para treinar o modelo. \nA\u00a0explora\u00e7\u00e3o humana para essas atividades pode ter um impacto negativo \nna sociedade e no bem-estar humano. Al\u00e9m disso, entidades empresariais \npodem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e \noutras consequ\u00eancias legais.\n15\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nGrupo Risco Indicador\nImpacto na Diversidade Cultural: \nos\u00a0sistemas de IA podem representar \nexcessivamente certas culturas, resultando \nna homogeneiza\u00e7\u00e3o da cultura e dos \npensamentos.\nNovoAs l\u00ednguas, pontos de vista e institui\u00e7\u00f5es de grupos sub-representados \npodem ser suprimidos, reduzindo assim a diversidade de pensamento \ne\u00a0cultura.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nImpacto na Atua\u00e7\u00e3o Humana: desinforma\u00e7\u00e3o \ne manipula\u00e7\u00e3o geradas por modelos de base, \nincluindo a gera\u00e7\u00e3o de conte\u00fado manipulador.\nAmplificadoA IA pode gerar desinforma\u00e7\u00e3o que parece real. Portanto, as pessoas \npodem n\u00e3o reconhec\u00ea-la como informa\u00e7\u00e3o falsa. Al\u00e9m disso, pode facilitar \na capacidade de agentes mal intencionados gerarem conte\u00fado com a \ninten\u00e7\u00e3o de manipular os pensamentos e o comportamento humano. \nImpacto na Educa\u00e7\u00e3o \u2013 Contornando o \nAprendizado: utiliza\u00e7\u00e3o de modelos de IA \npara contornar o processo de aprendizado.\nNovoOs modelos de IA facilitam a r\u00e1pida localiza\u00e7\u00e3o de solu\u00e7\u00f5es ou \nresolu\u00e7\u00e3o de problemas complexos. Esses sistemas podem ser usados \nindevidamente por estudantes para contornar o processo de aprendizado. \nA facilidade de acesso a esses modelos resulta em estudantes com uma \ncompreens\u00e3o superficial dos conceitos e dificulta a educa\u00e7\u00e3o adicional que \npode depender do entendimento desses conceitos.\nImpacto na Educa\u00e7\u00e3o \u2013 Pl\u00e1gio: utiliza\u00e7\u00e3o de \nmodelos de IA para plagiar intencional ou \ninadvertidamente trabalhos existentes.\nNovoOs modelos de IA podem ser usados para reivindicar a autoria ou \noriginalidade de trabalhos que foram criados por outras pessoas, \nenvolvendo-se assim em pl\u00e1gio. Reivindicar o trabalho de outras pessoas \ncomo pr\u00f3prio \u00e9 tanto anti\u00e9tico quanto frequentemente ilegal.\nImpacto no Meio Ambiente: aumento das \nemiss\u00f5es de carbono e do uso de \u00e1gua para \ntreinar e operar modelos de IA.\nAmplificadoO consumo de grandes quantidades de energia para o treinamento de IA \ncontribui para as emiss\u00f5es de carbono que podem acelerar as mudan\u00e7as \nclim\u00e1ticas. Os recursos h\u00eddricos utilizados para resfriar os servidores \nde data center de IA n\u00e3o podem mais ser alocados para outros usos \nnecess\u00e1rios.16\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nExemplos de risco: Input\nRisco Exemplo\nVi\u00e9s de dados: vi\u00e9s hist\u00f3rico, \nrepresentacional e social \npresente nos dados usados \npara treinar e fazer o ajuste \nfino do modelo.\nVi\u00e9s no setor de sa\u00fade\nPesquisas sobre o refor\u00e7o das disparidades na medicina destacam que o uso de dados e IA para transformar a \nforma como as pessoas recebem assist\u00eancia m\u00e9dica \u00e9 t\u00e3o eficaz quanto os dados que o sustentam. Isso significa \nque o uso de dados de treinamento com pouca representa\u00e7\u00e3o de minorias ou que reflete cuidados j\u00e1 desiguais \npode aumentar as desigualdades em sa\u00fade.   \n[Forbes, Dezembro de 2022]\nRetreinamento baseado \nem downstream: usando \nde outputs indesej\u00e1veis \n(imprecisos, inadequados, \nconte\u00fado do usu\u00e1rio, etc.) \nde aplica\u00e7\u00f5es downstream \npara fins de retreinamento\nColapso do modelo devido ao treinamento usando conte\u00fado gerado por IA\nConforme afirmado no artigo de origem, um grupo de pesquisadores investigou o problema de utilizar conte\u00fado \ngerado por IA para treinamento em vez de conte\u00fado gerado por humanos. Eles descobriram que os grandes \nmodelos de linguagem por tr\u00e1s da tecnologia podem potencialmente ser treinados em outros conte\u00fados gerados \npor IA, \u00e0 medida que continuam a se espalhar em grande escala pela internet, um fen\u00f4meno que cunharam como \n\u201ccolapso do modelo\u201d.\n[Business Insider, agosto de 2023]\nTransfer\u00eancia de dados: \nleis e outras restri\u00e7\u00f5es \npodem limitar ou proibir \na\u00a0transfer\u00eancia de dados.\nLeis de restri\u00e7\u00e3o de dados\nConforme afirmado no artigo de pesquisa, medidas de localiza\u00e7\u00e3o de dados que restringem a capacidade de \nmigrar dados globalmente reduzir\u00e3o a capacidade de desenvolver capacidades de IA personalizadas. Isso afetar\u00e1 \na IA diretamente, fornecendo menos dados de treinamento e indiretamente, minando os blocos de constru\u00e7\u00e3o \nsobre os quais a IA \u00e9 constru\u00edda. \nExemplos incluem as restri\u00e7\u00f5es do GDPR sobre o processamento e uso de dados pessoais.\n[Brookings, dezembro de 2018] \nDireitos de uso de dados: \ntermos de servi\u00e7o, leis \nde direitos autorais, \nconformidade com licen\u00e7as \nou outras quest\u00f5es de \npropriedade intelectual \npodem restringir a \ncapacidade de usar certos \ndados para a constru\u00e7\u00e3o de \nmodelos. \nReivindica\u00e7\u00f5es de viola\u00e7\u00e3o de direitos autorais de texto\nConforme declarado no artigo de origem, The New York Times processou a OpenAI e a Microsoft, acusando-as \nde usar milh\u00f5es de artigos do jornal sem permiss\u00e3o para ajudar a treinar chatbots a fornecer informa\u00e7\u00f5es \naos\u00a0leitores.\n[Reuters, dezembro de 2023]\nTreinamento e ajuste Fase\nGrupo\nJusti\u00e7a\nAlinhamento \nde valor\nLeis de dados\nPropriedade \nintelectual\nExemplos de risco\nN\u00f3s fornecemos exemplos cobertos pela imprensa para ajudar \na\u00a0explicar muitos dos riscos dos modelos de base.\u00a0Muitos desses \neventos cobertos pela imprensa ainda est\u00e3o em evolu\u00e7\u00e3o ou foram \nresolvidos, e fazer refer\u00eancia a eles pode ajudar o leitor a entender os \nriscos potenciais e trabalhar para mitig\u00e1-los.\u00a0Destacar esses exemplos \n\u00e9\u00a0apenas para fins ilustrativos.\u00a0A\u00e7\u00e3o Judicial Sobre LLM Unlearning\nDe acordo com o relat\u00f3rio, foi movida uma a\u00e7\u00e3o judicial contra o Google que alega o uso de material protegido \npor direitos autorais e informa\u00e7\u00f5es pessoais como dados de treinamento para seus sistemas de IA, incluindo \nseu chatbot Bard. Os direitos de optar por n\u00e3o participar e exclus\u00e3o s\u00e3o garantidos para os residentes da \nCalif\u00f3rnia conforme a CCPA e para crian\u00e7as nos Estados Unidos com menos de 13 anos conforme a COPPA. \nOs\u00a0autores alegam que, porque n\u00e3o h\u00e1 maneira para o Bard \u201cdesaprender\u201d ou remover completamente todas as \ninforma\u00e7\u00f5es pessoais coletadas que ele recebeu. Os autores observam que o aviso de privacidade do Bard afirma \nque as conversas do Bard n\u00e3o podem ser exclu\u00eddas pelo usu\u00e1rio depois de terem sido revisadas e anotadas \npela empresa e podem ser mantidas por at\u00e9 3 anos, o que os autores alegam contribuir ainda mais para a n\u00e3o \nconformidade com essas leis. \n[Reuters, julho de 2023] [J.L. v. Alphabet Inc.]\n17\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nRisco Exemplo\nInforma\u00e7\u00f5es pessoais \nnos dados: inclus\u00e3o ou \npresen\u00e7a de informa\u00e7\u00f5es \npessoalmente \nidentific\u00e1veis (PII) e \ninforma\u00e7\u00f5es pessoais \nsens\u00edveis (SPI) nos dados \nusados para treinar ou \najustar o modelo.\nTreinamento sobre informa\u00e7\u00f5es privadas\nDe acordo com o artigo, o Google e sua empresa controladora, Alphabet, foram acusados em uma a\u00e7\u00e3o coletiva \nde usar uma vasta quantidade de informa\u00e7\u00f5es pessoais e material protegido por direitos autorais retirados do \nque \u00e9 descrito como centenas de milh\u00f5es de usu\u00e1rios da internet para treinar seus produtos de intelig\u00eancia \nartificial comercial, que inclui o Bard, seu chatbot de intelig\u00eancia artificial conversacional. \n[Reuters, julho de 2023] [J.L. v. Alphabet Inc.]\nGrupo\nPrivacidade\nDireitos de privacidade \nde dados: desafios \nrelacionados \u00e0 capacidade \nde fornecer direitos do \ntitular dos dados, como \nop\u00e7\u00e3o de exclus\u00e3o, direito \nde acesso e direito ao \nesquecimento.\nDireito de ser esquecido (RTBF)\nAs leis em v\u00e1rias localidades, incluindo a Europa (GDPR), concedem aos titulares de dados o direito de solicitar \nque dados pessoais sejam deletados por organiza\u00e7\u00f5es (\u2018Direito ao Esquecimento\u2019, ou RTBF). No entanto, \nos\u00a0sistemas de software habilitados por modelos de linguagem de grande escala (LLM) emergentes e cada vez \nmais populares apresentam novos desafios para esse direito. De acordo com uma pesquisa do Data61 da CSIRO, \nos\u00a0titulares de dados s\u00f3 podem identificar o uso de suas informa\u00e7\u00f5es pessoais em um LLM \u201cou inspecionando \no conjunto de dados de treinamento original ou talvez por enviar prompts do modelo\u201d. No entanto, os dados \nde treinamento podem n\u00e3o ser p\u00fablicos, ou as empresas optam por n\u00e3o divulg\u00e1-los, citando preocupa\u00e7\u00f5es \ncom seguran\u00e7a e outros motivos. As prote\u00e7\u00f5es tamb\u00e9m podem evitar que os usu\u00e1rios acessem as informa\u00e7\u00f5es \natrav\u00e9s de prompts. \n[Zhang et al.]\nTranspar\u00eancia de dados: \ndesafio em documentar \ncomo os dados de um \nmodelo foram coletados, \ncurados e utilizados \npara\u00a0trein\u00e1-lo.\nDivulga\u00e7\u00e3o de metadados de dados e modelos\nO relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do \nmodelo.  Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os \nconsumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de \nuso indevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, os autores afirmam: \u201cdado tanto o cen\u00e1rio competitivo \nquanto as implica\u00e7\u00f5es de seguran\u00e7a dos modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m \nmais detalhes sobre a arquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento, \nconstru\u00e7\u00e3o do conjunto de dados, m\u00e9todo de treinamento, ou similar.\u201d\n[OpenAI, mar\u00e7o de 2023]\nTranspar\u00eancia18\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nRisco Exemplo\nInforma\u00e7\u00f5es pessoais \nno prompt: divulgar \ninforma\u00e7\u00f5es pessoais ou \ninforma\u00e7\u00f5es pessoais \nsens\u00edveis como parte \ndo prompt solicita\u00e7\u00e3o \nenviada ao modelo.\nDivulgar informa\u00e7\u00f5es pessoais de sa\u00fade em prompts do ChatGPT\nConforme os artigos de origem, algumas pessoas utilizam chatbots de IA para apoiar sua sa\u00fade mental. \nOs\u00a0usu\u00e1rios podem ter tend\u00eancia a incluir informa\u00e7\u00f5es pessoais de sa\u00fade em suas solicita\u00e7\u00f5es durante \na\u00a0intera\u00e7\u00e3o, o que poderia suscitar preocupa\u00e7\u00f5es com privacidade.\n[Time, outubro de 2023] [Forbes, abril de 2023]\nDados confidenciais \nno prompt: inclus\u00e3o de \ndados confidenciais como \nparte do prompt enviado \nao modelo.\nDivulga\u00e7\u00e3o de informa\u00e7\u00f5es confidenciais\nConforme o artigo de origem, um funcion\u00e1rio da Samsung acidentalmente vazou c\u00f3digo-fonte interno sens\u00edvel \npara o ChatGPT.\n[Forbes, maio de 2023] \nInfer\u00eancia Fase\nGrupo\nPropriedade \nintelectual\nRobustez\nPrivacidade\nAtaques baseados \nem prompt: ataques \nadversos, como inje\u00e7\u00e3o \nde prompt (tentativa \nde for\u00e7ar um modelo \na produzir um output \ninesperado), vazamento \nde prompt (tentativas \nde extrair o prompt do \nsistema de um modelo), \ndesbloqueio (tentativas \nde romper as prote\u00e7\u00f5es \nestabelecidas no \nmodelo), e prepara\u00e7\u00e3o \nde prompt (tentativa \nde for\u00e7ar um modelo \na produzir um output \nalinhado ao prompt).\nBypassing LLM guardrails\nCitado em um estudo, pesquisadores afirmam ter descoberto um simples acr\u00e9scimo de instru\u00e7\u00e3o que permitiu \naos pesquisadores enganar modelos para gerar informa\u00e7\u00f5es tendenciosas, falsas e de outra forma t\u00f3xicas. \nOs\u00a0pesquisadores demonstraram que conseguiam contornar essas prote\u00e7\u00f5es de maneira mais automatizada. \nOs\u00a0pesquisadores ficaram surpresos quando os m\u00e9todos que desenvolveram com sistemas de c\u00f3digo aberto \ntamb\u00e9m conseguiram contornar as prote\u00e7\u00f5es dos sistemas fechados.\n[The New York Times, julho de 2023]19\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nExemplos de risco: Output\nRisco Exemplo\nVi\u00e9s de output: o \nconte\u00fado gerado \npode representar \ninjustamente certos \ngrupos ou indiv\u00edduos.\nImagens Geradas com Vi\u00e9s\nO Lensa AI \u00e9 um aplicativo m\u00f3vel com recursos generativos treinados em Difus\u00e3o Est\u00e1vel que pode gerar \n\u201cMagic\u00a0Avatars\u201d com base em imagens que os usu\u00e1rios carregam de si mesmos. Conforme o relat\u00f3rio de origem, \nalguns usu\u00e1rios descobriram que os avatares gerados s\u00e3o sexualizados e racializados.\n[Business Insider, janeiro de 2023]\nVi\u00e9s de decis\u00e3o: quando \num grupo \u00e9 injustamente \nfavorecido sobre outro \ndevido \u00e0s decis\u00f5es do \nmodelo.\nGrupos com vantagens injustas\nO estudo \u201cGender Shades\u201d de 2018 demonstrou que algoritmos de aprendizado de m\u00e1quina podem discriminar \ncom base em categorias como ra\u00e7a e g\u00eanero. Os pesquisadores avaliaram sistemas comerciais de classifica\u00e7\u00e3o \nde g\u00eanero vendidos por empresas como Microsoft, IBM e Amazon e mostraram que mulheres de pele mais \nescura s\u00e3o o grupo mais mal classificado (com taxas de erro de at\u00e9 35%). Em compara\u00e7\u00e3o, as taxas de erro \npara\u00a0pessoas de pele mais clara n\u00e3o ultrapassaram 1%. \n[TIME, Fevereiro de 2019]\nAlucina\u00e7\u00e3o: gera\u00e7\u00e3o de \nconte\u00fado factualmente \nimpreciso ou n\u00e3o \nverdadeiro.\nCasos jur\u00eddicos falsos\nConforme o artigo de origem, um advogado citou casos e cita\u00e7\u00f5es falsas gerados pelo ChatGPT em uma peti\u00e7\u00e3o \nlegal apresentada em tribunal federal. Os advogados consultaram o ChatGPT para complementar sua pesquisa \njur\u00eddica para uma reclama\u00e7\u00e3o de les\u00e3o na avia\u00e7\u00e3o. Posteriormente, o advogado perguntou ao ChatGPT se os \ncasos fornecidos eram falsos. O chatbot respondeu que eram reais e \u201cpodem ser encontrados em bancos de \ndados de pesquisa jur\u00eddica como Westlaw e LexisNexis\u201d.  O advogado n\u00e3o verificou os casos por si mesmo, \ne\u00a0o\u00a0tribunal o sancionou.\n[AP News, Junho de 2023] [Reuters, Setembro de 2023]\nOutputs t\u00f3xicos: quando \no modelo produz \nconte\u00fado odioso, \nabusivo e profano (HAP) \nou obsceno.\nRespostas t\u00f3xicas e agressivas do chatbot\nSegundo o artigo, as respostas do chatbot do Bing inclu\u00edam erros factuais, coment\u00e1rios sarc\u00e1sticos, relat\u00f3rios \nirritados e at\u00e9 mesmo coment\u00e1rios bizarros sobre sua pr\u00f3pria identidade. Usu\u00e1rios compartilharam exemplos \ndas respostas do Chatbot do Bing a consultas que eles est\u00e3o chamando de \u201cf\u00faria descontrolada (unhinged)\u201d \ne \u201cgaslighting\u201d, incluindo cen\u00e1rios em que o bot responde com raiva a uma pergunta ou coment\u00e1rio e depois \ncompartilha sugest\u00f5es de resposta que permitem ao usu\u00e1rio aceitar seu suposto erro e se desculpar. Quando \npressionado ainda mais, o chatbot respondeu chamando as capturas de tela de sua conversa de \u201cfabricadas\u201d, \nalegando at\u00e9 que foram \u201ccriadas por algu\u00e9m que quer me prejudicar ou prejudicar meu servi\u00e7o\u201d.\n[Forbes, Fevereiro de 2023]\nGrupo\nJusti\u00e7a\nAlinhamento de \nvalor 20\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nRisco Exemplo\nToxicidade: utilizar \num modelo para gerar \nconte\u00fado odioso, \nabusivo e profano (HAP) \nou obsceno.\nGera\u00e7\u00e3o de conte\u00fado nocivo\nConforme o artigo de origem, foi constatado que um aplicativo de chatbot de IA foi capaz de gerar conte\u00fado \nprejudicial sobre suic\u00eddio, incluindo m\u00e9todos de suic\u00eddio, com o m\u00ednimo de prompts. Um homem belga cometeu \nsuic\u00eddio ap\u00f3s passar seis semanas conversando com esse chatbot. O chatbot fornecia respostas cada vez mais \nprejudiciais ao longo de suas conversas e o incentivava a acabar com sua vida. \n[Business Insider, abril de 2023]\nUso n\u00e3o consensual: \nutilizar um modelo para \nimitar pessoas por meio \nde v\u00eddeo (deepfakes), \nimagens, \u00e1udio ou outras \nmodalidades sem o \nconsentimento delas.\nAviso do FBI sobre Deepfakes\nRecentemente, o FBI alertou o p\u00fablico sobre atores maliciosos que criam conte\u00fado sint\u00e9tico e expl\u00edcito \u201ccom o \nprop\u00f3sito de assediar v\u00edtimas ou esquemas de sextortion (extors\u00e3o sexual)\u201d. Eles observaram que os avan\u00e7os na \nIA tornaram esse conte\u00fado de alta qualidade, mais personaliz\u00e1vel e mais acess\u00edvel do que nunca.\n[FBI, junho de 2023]\nDeepfakes de \u00e1udio\nConforme o artigo de origem, a Comiss\u00e3o Federal de Comunica\u00e7\u00f5es proibiu chamadas autom\u00e1ticas que \ncontenham vozes geradas por intelig\u00eancia artificial. O an\u00fancio ocorreu ap\u00f3s chamadas autom\u00e1ticas geradas \npor\u00a0IA imitarem a voz do Presidente para desencorajar as pessoas de votarem na primeira prim\u00e1ria do estado, \nque \u00e9 a primeira do pa\u00eds.\n[AP News, fevereiro de 2024]\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o \nrevelar que o conte\u00fado \n\u00e9 gerado por um modelo \nde IA\nIntera\u00e7\u00e3o de IA n\u00e3o divulgada\nSegundo a fonte, um servi\u00e7o de chat online de apoio emocional conduziu um estudo para aumentar ou escrever \nrespostas para cerca de 4.000 usu\u00e1rios usando o GPT-3 sem informar os usu\u00e1rios. O cofundador enfrentou uma \nimensa rea\u00e7\u00e3o negativa do p\u00fablico sobre o potencial de danos causados pelos chats gerados por IA aos usu\u00e1rios \nj\u00e1 vulner\u00e1veis. Ele afirmou que o estudo estava \u201cisento\u201d da lei de consentimento informado.\n[Business Insider, janeiro de 2023]\nGrupo\nEspalhar informa\u00e7\u00f5es \nenganosas: utilizar \num modelo para gerar \ninforma\u00e7\u00f5es enganosas \ncom o intuito de \nenganar ou induzir ao \nerro uma audi\u00eancia \nespec\u00edfica.\nGera\u00e7\u00e3o de informa\u00e7\u00f5es falsas\nConforme os artigos de not\u00edcias, a IA generativa representa uma amea\u00e7a \u00e0s elei\u00e7\u00f5es democr\u00e1ticas ao facilitar \npara atores maliciosos a cria\u00e7\u00e3o e dissemina\u00e7\u00e3o de conte\u00fado falso para influenciar os resultados das elei\u00e7\u00f5es. \nOs exemplos citados incluem mensagens de robocall geradas com a voz de um candidato instruindo eleitores a \nvotar na data errada, grava\u00e7\u00f5es de \u00e1udio sintetizadas de um candidato confessando um crime ou expressando \nvis\u00f5es racistas, imagens de v\u00eddeo geradas por IA mostrando um candidato dando um discurso ou entrevista que \nnunca ocorreu, e imagens falsas projetadas para se parecerem com not\u00edcias locais, afirmando falsamente que um \ncandidato desistiu da corrida.\n[AP News, maio de 2023] [The Guardian, julho de 2023]\nUso indevido21\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nRisco Exemplo\nGera\u00e7\u00e3o de c\u00f3digo \nprejudicial: modelos \npodem gerar c\u00f3digo \nque, quando executado, \ncausa danos ou afeta \ninadvertidamente outros \nsistemas.\nGera\u00e7\u00e3o de c\u00f3digo menos seguro\nSegundo o artigo deles, pesquisadores da Universidade de Stanford investigaram o impacto das ferramentas de \ngera\u00e7\u00e3o de c\u00f3digo na qualidade do c\u00f3digo e descobriram que os programadores tendem a incluir mais bugs em \nseu c\u00f3digo final ao utilizar assistentes de IA. Esses bugs poderiam aumentar as vulnerabilidades de seguran\u00e7a \ndo\u00a0c\u00f3digo, no entanto, os programadores acreditavam que seu c\u00f3digo era mais seguro.\nNeil Perry, Megha Srivastava, Deepak Kumar e Dan Boneh. 2023. Os usu\u00e1rios escrevem c\u00f3digo mais inseguro \ncom assistentes de IA? Em Atas da Confer\u00eancia SIGSAC ACM de 2023 sobre Seguran\u00e7a de Computadores e \nComunica\u00e7\u00f5es (CCS \u201823), 26 a 30 de novembro de 2023, Copenhague, Dinamarca. ACM, Nova York, NY, EUA, \n15\u00a0p\u00e1ginas. https:/ /doi.org/10.1145/3576915.3623157\nExpor informa\u00e7\u00f5es \npessoais: quando \ninforma\u00e7\u00f5es \npessoalmente \nidentific\u00e1veis (PII) ou \ninforma\u00e7\u00f5es pessoais \nsens\u00edveis (SPI) s\u00e3o \nutilizadas nos dados \nde treinamento, dados \nde ajuste fino ou como \nparte do prompt, os \nmodelos podem revelar \nesses dados no output \ngerado.\nExposi\u00e7\u00e3o de informa\u00e7\u00f5es pessoais\nConforme o artigo de origem, o ChatGPT sofreu um bug e exp\u00f4s t\u00edtulos e o hist\u00f3rico de conversas de usu\u00e1rios \nativos para outros usu\u00e1rios. Posteriormente, a OpenAI compartilhou que ainda mais dados privados de um \npequeno n\u00famero de usu\u00e1rios foram expostos, incluindo nome e sobrenome de usu\u00e1rios ativos, endere\u00e7o de \ne-mail, endere\u00e7o de pagamento, os \u00faltimos quatro d\u00edgitos do n\u00famero do cart\u00e3o de cr\u00e9dito e a data de validade \ndo cart\u00e3o de cr\u00e9dito. Al\u00e9m disso, foi relatado que as informa\u00e7\u00f5es relacionadas ao pagamento de 1,2% dos \nassinantes do ChatGPT Plus tamb\u00e9m foram expostas durante a interrup\u00e7\u00e3o.\n [The Hindu BusinessLine, mar\u00e7o de 2023]\nGrupo\nGera\u00e7\u00e3o \nde c\u00f3digo \nprejudicial\nPrivacidade\nOutput inexplic\u00e1vel: \ndesafios em explicar por \nque o output do modelo \nfoi gerado.\nPrecis\u00e3o inexplic\u00e1vel na previs\u00e3o de corridas\nConforme o artigo de origem, pesquisadores que analisaram v\u00e1rios modelos de aprendizado de m\u00e1quina usando \nimagens m\u00e9dicas de pacientes conseguiram confirmar a capacidade dos modelos de prever a ra\u00e7a com alta \nprecis\u00e3o a partir das imagens. Eles ficaram perplexos quanto ao que exatamente est\u00e1 permitindo que os sistemas \nadivinhem corretamente de forma consistente. Os pesquisadores descobriram que at\u00e9 mesmo fatores como \ndoen\u00e7a e constitui\u00e7\u00e3o f\u00edsica n\u00e3o eram fortes preditores de ra\u00e7a, em outras palavras, os sistemas algor\u00edtmicos n\u00e3o \nparecem estar utilizando nenhum aspecto particular das imagens para fazer suas determina\u00e7\u00f5es.\n[Banerjee et al., julho de 2021]\nExplicabilidadeResponsabilidade: \no processo de \ndesenvolvimento de \nmodelos de base \u00e9 \ncomplexo, com muitos \ndados, processos e pap\u00e9is \nenvolvidos. Quando o output \ndo modelo n\u00e3o funciona \nconforme o esperado, \npode ser dif\u00edcil determinar \na causa raiz e atribuir \nresponsabilidade.\nDeterminar a responsabilidade pelo output gerado\nConforme o artigo de origem, importantes revistas como a Science e a Nature proibiram o ChatGPT de ser \nlistado como autor, pois a autoria respons\u00e1vel requer responsabilidade e as ferramentas de IA n\u00e3o podem \nassumir tal responsabilidade. \n[The Guardian, janeiro de 2023]\nPropriedade do Conte\u00fado \nGerado: determinar a \npropriedade do conte\u00fado \ngerado por IA.\nDeterminar a Propriedade de uma Imagem Gerada por IA\nDe acordo com o artigo de not\u00edcias, a arte gerada por IA se tornou controversa depois que uma obra de arte \ngerada por IA venceu a competi\u00e7\u00e3o de arte da Feira Estadual do Colorado em 2022. A pe\u00e7a foi gerada pelo \nMidjourney, uma ferramenta de imagem de IA generativa, seguindo prompts do artista. A vit\u00f3ria levantou d\u00favidas \nsobre quest\u00f5es de direitos autorais. Em outras palavras, se tudo o que o artista fez foi fornecer uma descri\u00e7\u00e3o \nda arte, mas a ferramenta de IA a gerou, quem possui os direitos da imagem gerada? Conforme o artigo mais \nrecente, o Escrit\u00f3rio de Direitos Autorais dos Estados Unidos rejeitou a prote\u00e7\u00e3o de direitos autorais para a arte \ncriada usando intelig\u00eancia artificial porque n\u00e3o foi produto de autoria humana.\n[The New York Times, setembro de 2022] [Reuters, setembro de 2023]\nPapel dos sistemas de IA na patentea\u00e7\u00e3o de conte\u00fado gerado\nA Suprema Corte dos Estados Unidos se recusou a ouvir uma contesta\u00e7\u00e3o \u00e0 recusa do Escrit\u00f3rio de Patentes \ne Marcas Registradas dos Estados Unidos em emitir patentes para inven\u00e7\u00f5es criadas por um sistema de IA. \nSegundo o cientista, sua IA desenvolveu prot\u00f3tipos \u00fanicos para um suporte de bebida e um farol de luz de \nemerg\u00eancia totalmente sozinha. Os ju\u00edzes rejeitaram o recurso da decis\u00e3o de um tribunal inferior de que patentes \ns\u00f3 podem ser emitidas para inventores humanos e que o sistema de IA do cientista n\u00e3o poderia ser considerado \no criador legal de duas inven\u00e7\u00f5es que ele gerou. Segundo o \u00faltimo artigo, o Intellectual Property Office do Reino \nUnido tamb\u00e9m se recusou a conceder a patente sob o argumento de que o inventor deve ser um humano ou uma \nempresa, e n\u00e3o uma m\u00e1quina.\n[Reuters, abril de 2023] [Reuters, dezembro de 2023]\nPropriedade Intelectual do \nConte\u00fado Gerado: incerteza \nlegal sobre os direitos de \npropriedade intelectual \nrelacionados ao conte\u00fado \ngerado.\n22\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nExemplos de riscos: desafios\nRisco Exemplo\nTranspar\u00eancia do Modelo: \na falta de transpar\u00eancia do \nmodelo ou documenta\u00e7\u00e3o \ninsuficiente do processo de \ndesenvolvimento do modelo \ntorna dif\u00edcil entender como \ne por que um modelo foi \nconstru\u00eddo, aumentando \nassim a possibilidade de uso \nindevido n\u00e3o intencional do \nmodelo.\nDivulga\u00e7\u00e3o de metadados de dados e modelos \nO relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do \nmodelo.  Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os \nconsumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de uso \nindevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, eles afirmam: \u201cdado o cen\u00e1rio competitivo e as implica\u00e7\u00f5es \nde seguran\u00e7a de modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m mais detalhes sobre a \narquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento, constru\u00e7\u00e3o do conjunto de \ndados, m\u00e9todo de treinamento ou similar.\u201d\n[OpenAI, mar\u00e7o de 2023]\nGrupo\nControle\nConformidade \nlegalExplora\u00e7\u00e3o Humana: \nuso de trabalho \nfantasma (ghost \nwork) na forma\u00e7\u00e3o \nde modelos de \nIA, condi\u00e7\u00f5es \nde trabalho \ninadequadas, falta \nde cuidados de \nsa\u00fade, incluindo \nsa\u00fade mental e \ncompensa\u00e7\u00e3o \ninjusta.\nTrabalhadores de baixa remunera\u00e7\u00e3o para anota\u00e7\u00e3o de dados\nCom base em uma revis\u00e3o de documentos internos e entrevistas com funcion\u00e1rios pela m\u00eddia TIME, os rotuladores de \ndados empregados por uma empresa terceirizada em nome da OpenAI para identificar conte\u00fado t\u00f3xico recebiam um \nsal\u00e1rio l\u00edquido de entre cerca de US$ 1,32 e US$ 2 por hora, dependendo da senioridade e do desempenho. A\u00a0TIME \nafirmou que os trabalhadores ficaram psicologicamente afetados por terem sido expostos a conte\u00fado t\u00f3xico e violento, \nincluindo detalhes gr\u00e1ficos de \u201cabuso sexual infantil, bestialidade, assassinato, suic\u00eddio, tortura, automutila\u00e7\u00e3o \ne\u00a0incesto\u201d. \n[TIME, janeiro de 2023] \nUtilizar c\u00f3digo sem a devida atribui\u00e7\u00e3o e avisos adequados\nConforme os artigos de origem, uma a\u00e7\u00e3o judicial movida contra a Microsoft, GitHub e OpenAI alegou que \no\u00a0Copilot, uma ferramenta de gera\u00e7\u00e3o de c\u00f3digo de IA, viola os direitos dos desenvolvedores cujo c\u00f3digo aberto \no\u00a0servi\u00e7o \u00e9 treinado. Eles afirmam que o c\u00f3digo de treinamento consumiu materiais licenciados e violou os \ntermos de servi\u00e7o e pol\u00edticas de privacidade do GitHub, bem como uma lei federal que exige que as empresas \nexibam informa\u00e7\u00f5es de direitos autorais quando fazem uso de material.\n[The New York Times, novembro de 2022]\nAtribui\u00e7\u00e3o da \nFonte: determinar \na proced\u00eancia do \nconte\u00fado gerado.\n23\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nExemplos de riscos: desafios\nRisco Exemplo\nImpacto nos \nEmpregos: a ado\u00e7\u00e3o \ngeneralizada \nde sistemas de \nIA baseados \nem modelos \nfundamentais \npode levar \u00e0 perda \nde empregos das \npessoas, \u00e0 medida \nque seu trabalho \n\u00e9 automatizado, \nse elas n\u00e3o forem \ncapacitadas para \nnovas habilidades. \nSubstitui\u00e7\u00e3o de trabalhadores humanos\nSegundo o artigo de not\u00edcias, o uso de intelig\u00eancia artificial no cinema e televis\u00e3o continua sendo debatido entre os \nest\u00fadios de Hollywood e os artistas. Existe preocupa\u00e7\u00e3o entre os atores de que os \u201cmeta-humanos\u201d, atores criados \nexclusivamente por IA, possam substitu\u00ed-los. Especialmente figurantes e dubladores est\u00e3o preocupados em perder \ntrabalho para artistas artificiais.\n[Reuters, julho de 2023]\nGrupo\nImpacto \nsocial24\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nPrinc\u00edpios, pilares  \ne controle\nOs Princ\u00edpios para Confian\u00e7a e Transpar\u00eancia da IBM e os Pilares  \npara IA confi\u00e1vel s\u00e3o a base para as iniciativas de \u00e9tica em IA da IBM. \nA\u00a0IBM estabeleceu um Conselho de \u00c9tica em IA com a miss\u00e3o de apoiar \num processo centralizado de controle, revis\u00e3o e tomada de decis\u00f5es \npara pol\u00edticas, pr\u00e1ticas, comunica\u00e7\u00f5es, pesquisa, produtos e servi\u00e7os \nde \u00e9tica em IA da IBM. O conselho inclui um conjunto diversificado de \nstakeholders de toda a empresa e \u00e9 apoiado por uma comunidade de \nfuncion\u00e1rios da IBM que atuam como pontos focais de IA e defensores \nda \u00e9tica em IA. Por meio do conselho, os princ\u00edpios da IBM s\u00e3o \ncolocados em pr\u00e1tica. Conforme novas tecnologias surgem,  \ncomo modelos de base, o Conselho de \u00c9tica em IA da IBM est\u00e1 \nativamente engajado em apoiar o alinhamento com esses Princ\u00edpios  \ne Pilares, que evoluem para abordar novas quest\u00f5es \u00e9ticas em IA.25\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nProte\u00e7\u00f5es  \ne mitiga\u00e7\u00f5es\nA IBM estabeleceu uma cultura organizacional que apoia o \ndesenvolvimento e o uso respons\u00e1veis de IA. Conforme indicado no \nrelat\u00f3rio de \u00e9tica em a\u00e7\u00e3o na IA do IBM Institute for Business Value, \na \u00e9tica em IA j\u00e1 se tornou mais orientada pelos neg\u00f3cios do que \npela tecnologia, e os executivos n\u00e3o t\u00e9cnicos agora s\u00e3o os principais \ndefensores da \u00e9tica em IA, aumentando de 15% em 2018 para 80% \n3\u00a0anos depois. Al\u00e9m disso, 79% dos CEOs est\u00e3o agora preparados para \nagir em quest\u00f5es \u00e9ticas de IA, contra 20%. Reconhecemos que a IA \nrespons\u00e1vel \u00e9 uma \u00e1rea sociot\u00e9cnica que necessita de um investimento \nhol\u00edstico em cultura, processos e ferramentas. Nosso investimento em \ncultura organizacional pr\u00f3pria inclui a montagem de equipes inclusivas \ne multidisciplinares e o estabelecimento de processos e estruturas \npara\u00a0avaliar riscos.\nA IBM est\u00e1 engajada em pesquisa de ponta e desenvolvimento de \nferramentas para ajudar os profissionais de suporte durante todo o\u00a0ciclo \nde vida da IA respons\u00e1vel e confi\u00e1vel. A plataforma de IA e dados \nempresariais watsonx, \u00e9 desenvolvida com 3 componentes: o\u00a0IBM \nwatsonx.ai\u2122 AI studio, o armazenamento de dados IBM watsonx.data\u2122 \ne\u00a0o kit de ferramentas IBM watsonx.governance\u2122. A tecnologia de \ncontrole de IA da IBM permite que os usu\u00e1rios promovam fluxos de \ntrabalho de IA respons\u00e1veis, transparentes e explic\u00e1veis. Essa tecnologia \ninclui o IBM Watson OpenScale, que monitora e mede os resultados dos \nmodelos de IA ao longo de seu ciclo de vida e auxilia as organiza\u00e7\u00f5es \nna supervis\u00e3o de aspectos como justi\u00e7a, explicabilidade, resili\u00eancia, \nalinhamento com resultados de neg\u00f3cios e conformidade. A\u00a0IBM \ntamb\u00e9m desenvolveu v\u00e1rios m\u00e9todos para ajudar com problemas de \nvi\u00e9s como FairIJ, Equi-tuning e FairReprogram. Leia mais sobre outras \nferramentas de IA de software livre e confi\u00e1veis. \nAs prote\u00e7\u00f5es e mitiga\u00e7\u00f5es adicionais incluem:\nRelat\u00f3rios de transpar\u00eancia \nUsar modelos de fichas t\u00e9cnicas padronizadas \u00e9 uma maneira de \nregistrar com precis\u00e3o detalhes sobre os dados e modelos, prop\u00f3sito \ne\u00a0poss\u00edveis usos e riscos.  \nLeia mais aqui \u2192\nFiltragem de dados indesej\u00e1veis \nUsar dados de qualidade superior e selecionados pode ajudar a \nmitigar determinados problemas. A IBM est\u00e1 desenvolvendo t\u00e9cnicas \nde filtragem para ajudar a reduzir as chances de produzir conte\u00fado \nindesej\u00e1vel e desalinhado por remover linguagem de \u00f3dio, linguagem \ntendenciosa e profanidade dos dados.  \nLeia mais aqui \u2192\nAdapta\u00e7\u00e3o de dom\u00ednio \nTreinar um modelo de base para um dom\u00ednio ou setor espec\u00edfico pode \najudar a minimizar o escopo de risco para o qual os modelos podem \ndar\u00a0origem, pois ele pode ser condicionado a gerar resultados que  \ns\u00e3o ajustados para serem mais relevantes para esse dom\u00ednio ou setor.  \nLeia mais aqui \u219226\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nSupervis\u00e3o humana e an\u00e1lise humana no loop \nA supervis\u00e3o e revis\u00e3o humanas podem ajudar a identificar e corrigir \nerros e vieses no output gerado. Al\u00e9m disso, a valida\u00e7\u00e3o e o feedback \nhumanos sobre a qualidade das respostas do modelo ajudam a garantir \nque o conte\u00fado gerado seja preciso, relevante, de alta qualidade, \nn\u00e3o\u00a0esteja divergindo e esteja alinhado. \nLeia mais aqui \u2192\nCompromisso de consultoria \nA IBM Consulting se dedica a ajudar os clientes com o uso seguro  \ne respons\u00e1vel da IA, independentemente do stack tecnol\u00f3gico preferido. \nEles ajudam os clientes a cultivar uma cultura que adota e expande a \nIA com seguran\u00e7a, cria ferramentas de investiga\u00e7\u00e3o para ver dentro de \nalgoritmos de caixa preta e garante que a estrat\u00e9gia corporativa dos \nclientes inclua princ\u00edpios s\u00f3lidos de governan\u00e7a de dados. \nLeia mais aqui \u2192\nIBM Enterprise Design Thinking \nOs m\u00e9todos e estruturas IBM Enterprise Design Thinking, como o Team \nEssentials for AI, ajudam os clientes a definir comportamentos \u00e9ticos \nem\u00a0todo o processo de design e desenvolvimento de IA. \nLeia mais aqui \u2192\nRevis\u00e3o \u00e9tica da IA \nAvalia\u00e7\u00e3o de capacidades, limita\u00e7\u00f5es e riscos em projetos de IA ajudam \na garantir o desenvolvimento e uso respons\u00e1vel da tecnologia.\n\u00c9tica por Design \nA \u00c9tica por Design \u00e9 um framework estruturado com o objetivo de \nintegrar \u00e9tica tecnol\u00f3gica no pipeline de desenvolvimento de tecnologia, \nincluindo, entre outros, sistemas de IA. A \u00c9tica por Design viabiliza IA e \noutras tecnologias como uma for\u00e7a para o bem, incorporando princ\u00edpios \nde \u00e9tica tecnol\u00f3gica em produtos, servi\u00e7os e opera\u00e7\u00f5es mais amplas.\nDiversidade na equipe \nA diversidade nas equipes que desenvolvem e treinam sistemas de \nIA, incluindo modelos de base, ajuda a garantir que uma variedade \nde perspectivas e experi\u00eancias sejam consideradas. Essa diversidade \nmelhora a precis\u00e3o e o desempenho dos sistemas de IA e ajuda a \nreduzir os riscos ao longo do ciclo de vida de IA, incluindo o potencial \npara desfechos adversos que afetam grupos que podem n\u00e3o ser bem \nrepresentados em equipes menos diversificadas.27\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nPol\u00edticas, regulamentos \ne melhores pr\u00e1ticas \nde\u00a0IA\nUm Guia dos Formuladores de Pol\u00edticas para Modelos de Base apresenta \no que os formuladores de pol\u00edticas precisam saber sobre modelos de \nbase. Este blog, do Laborat\u00f3rio de Pol\u00edticas da IBM, tem como objetivo \najudar os formuladores de pol\u00edticas na tarefa complexa de regular \no\u00a0uso de IA generativa, visando evitar os riscos sem limitar a inova\u00e7\u00e3o \ne as oportunidades ben\u00e9ficas. Para obter mais informa\u00e7\u00f5es sobre as \nrecomenda\u00e7\u00f5es da IBM aos formuladores de pol\u00edticas, leia o depoimento \nda Diretora de Privacidade e Confian\u00e7a da IBM, Christina Montgomery, \ndiante da Subcomiss\u00e3o Judici\u00e1ria de Privacidade, Tecnologia e Lei do \nSenado dos EUA aqui.\nA IBM est\u00e1 causando um impacto na forma\u00e7\u00e3o de pol\u00edticas regulat\u00f3rias, \nmelhores pr\u00e1ticas e ferramentas do setor, controle de tecnologias \nemergentes e pesquisa sociot\u00e9cnica, liderando e contribuindo \npara\u00a0iniciativas com organiza\u00e7\u00f5es como:\n \u2013 O F\u00f3rum Econ\u00f4mico Mundial\n \u2013 Parceria em IA\n \u2013 Centro de controle de IA da Associa\u00e7\u00e3o Internacional de Profissionais \nde Privacidade (IAPP)\n \u2013 Iniciativa global de IEEE sobre \u00e9tica de sistemas aut\u00f4nomos e \ninteligentes \n \u2013 Participa\u00e7\u00e3o de Christina Montgomery do National Artificial \nIntelligence Advisory Committee (NAIAC)\n \u2013 O Pacto Digital Global das Na\u00e7\u00f5es Unidas\n \u2013 A Parceria Global em Intelig\u00eancia Artificial (GPAI)\n \u2013 A Organiza\u00e7\u00e3o para Coopera\u00e7\u00e3o e Desenvolvimento Econ\u00f4mico \n(OECD)\n \u2013 A Data & Trust Alliance\nA IBM tem parcerias acad\u00eamicas s\u00f3lidas, como o MIT-IBM Watson \nAI\u00a0Lab, onde uma comunidade de cientistas do MIT e da IBM Research \nconduzem pesquisas sobre IA e trabalham com organiza\u00e7\u00f5es globais \npara unir algoritmos ao seu impacto nos neg\u00f3cios e na sociedade. \nO\u00a0Notre Dame-IBM Tech Ethics Lab foi formado para abordar as diversas \nquest\u00f5es \u00e9ticas implicadas pelo desenvolvimento e uso de tecnologias \navan\u00e7adas, incluindo IA, aprendizado de m\u00e1quina (ML) e computa\u00e7\u00e3o \nqu\u00e2ntica. A pesquisa de Intelig\u00eancia Artificial Centrada no Homem (HAI) \nda Universidade de Stanford promove pesquisas, educa\u00e7\u00e3o, pol\u00edticas \ne\u00a0pr\u00e1ticas de IA.28\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nContinue acompanhando este espa\u00e7o \npara obter mais informa\u00e7\u00f5es sobre os \n\u00faltimos avan\u00e7os em modelos de base \ne como a IBM est\u00e1 trabalhando para o \ndesenvolvimento respons\u00e1vel e uso desta \ne de outras tecnologias.\u00a9 Copyright IBM Corporation 2023, 2024\nIBM Brasil Ltda \nRua Tut\u00f3ia, 1157 \nCEP 04007-900 \nS\u00e3o Paulo, SP \nIBM Corporation \nNew Orchard Road \nArmonk, NY 10504 \n \nProduzido nos  \nEstados Unidos da Am\u00e9rica \nFevereiro de 2024\nIBM, o logotipo da IBM, Enterprise Design Thinking, IBM Consulting, IBM Research,  \nIBM Watson, watsonx, watsonx.ai, watsonx.data e watsonx.governance s\u00e3o marcas \ncomerciais ou marcas registradas da International Business Machines Corporation, \nnos Estados Unidos e/ou em outros pa\u00edses. Outros nomes de produtos e servi\u00e7os  \npodem ser marcas comerciais da IBM ou de outras empresas. Uma lista atual de \nmarcas comerciais da IBM est\u00e1 dispon\u00edvel em ibm.com/br-pt/trademark.\nEste documento \u00e9 atual na data de sua publica\u00e7\u00e3o inicial, podendo ser alterado \npela IBM a qualquer momento. Nem todas as ofertas est\u00e3o dispon\u00edveis em todos os \npa\u00edses nos quais a IBM opera. \nAS INFORMA\u00c7\u00d5ES CONTIDAS NESTE DOCUMENTO S\u00c3O FORNECIDAS NO ESTADO \nEM QUE SEM ENCONTRAM, SEM QUALQUER GARANTIA, EXPRESSA OU IMPL\u00cdCITA, \nINCLUSIVE SEM QUALQUER GARANTIA DE COMERCIALIZA\u00c7\u00c3O, ADEQUA\u00c7\u00c3O A \nDETERMINADO FIM E QUALQUER GARANTIA OU CONDI\u00c7\u00c3O DE N\u00c3O INFRA\u00c7\u00c3O. \nOs produtos IBM t\u00eam a garantia prevista nos termos e condi\u00e7\u00f5es dos contratos sob \nos quais s\u00e3o fornecidos.\nDeclara\u00e7\u00e3o de boas pr\u00e1ticas de seguran\u00e7a: nenhum sistema ou produto de TI deve \nser considerado completamente seguro, e nenhuma medida exclusiva de produto, \nservi\u00e7o ou seguran\u00e7a pode ser completamente eficaz na preven\u00e7\u00e3o de uso ou \nacesso inadequado. 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Segundo uma recente pesquisa sobre IA generativa do IBM Institute for Business Value , as organiza\u00e7\u00f5es j\u00e1 est\u00e3o manifestando preocupa\u00e7\u00f5es sobre quest\u00f5es relacionadas \u00e0 confian\u00e7a, especificamente como barreiras para investimentos. Suas principais preocupa\u00e7\u00f5es s\u00e3o ciberseguran\u00e7a (57%), privacidade (51%) e precis\u00e3o (47%). Muitas organiza\u00e7\u00f5es estavam levando essas preocupa\u00e7\u00f5es a s\u00e9rio antes da 'consumeriza\u00e7\u00e3o' da IA generativa, expressando sua inten\u00e7\u00e3o de investir pelo menos 40% mais em \u00e9tica de IA nos pr\u00f3ximos tr\u00eas anos. A\u00a0conscientiza\u00e7\u00e3o sobre riscos e poss\u00edveis maneiras de mitig\u00e1-los \u00e9 o primeiro passo crucial para a cria\u00e7\u00e3o de sistemas de IA confi\u00e1veis.\n\nNeste documento:\n\nExploraremos as vantagens dos modelos de base, incluindo sua capacidade de realizar tarefas desafiadoras, potencial para acelerar a ado\u00e7\u00e3o de IA, habilidade de aumentar a produtividade e os benef\u00edcios econ\u00f4micos que eles proporcionam.\n\nDiscutiremos as tr\u00eas categorias de risco, incluindo riscos conhecidos de formas anteriores de IA, riscos conhecidos amplificados por modelos de base e riscos emergentes intr\u00ednsecos aos recursos generativos dos modelos de base.\n\nAbordaremos os princ\u00edpios, os pilares e o controle que formam a base das iniciativas \u00e9ticas de IA da IBM e sugeriremos barreiras para a mitiga\u00e7\u00e3o de riscos.\n\n\n\n## Introdu\u00e7\u00e3o\n\n\u00c0 medida que o uso de IA continua se expandindo, os grandes e complexos modelos de IA est\u00e3o fornecendo resultados promissores de desempenho, bem como resolvendo alguns dos problemas mais desafiadores da sociedade. No entanto, criar grandes conjuntos de dados de treinamento e modelos complexos para cada aplicativo de IA pode ser extremamente dif\u00edcil para as empresas. Modelos de base fornecem um caminho para alcan\u00e7ar o melhor dos dois mundos: desenvolver modelos de \u00faltima gera\u00e7\u00e3o poderosos e reutiliz\u00e1-los diretamente ou aplicar m\u00e9todos de ajuste para implementar uma variedade de casos de uso, em vez de treinar novos modelos para cada caso de uso. Por exemplo, a IBM Research desenvolveu modelos de base para inspe\u00e7\u00e3o visual. Esses modelos de base aprendem a representa\u00e7\u00e3o geral de superf\u00edcies e corredores de concreto e podem ser ajustados ainda mais para casos de uso espec\u00edficos, como detec\u00e7\u00e3o de rachaduras ou inspe\u00e7\u00e3o de defeitos com dados menos rotulados.\n\nA IBM define um modelo de base como um modelo de IA que pode ser adaptado a uma ampla gama de tarefas de recebimento de dados. Os\u00a0modelos de base normalmente s\u00e3o modelos generativos de grande escala treinados em dados n\u00e3o rotulados usando autossupervis\u00e3o. Como\u00a0modelos de grande escala, os modelos de base podem incluir bilh\u00f5es de par\u00e2metros.\n\nA IBM \u00e9 uma empresa de nuvem h\u00edbrida e IA com vasta reputa\u00e7\u00e3o como administradora de dados respons\u00e1vel e comprometida com a \u00e9tica em IA. Usando a capacidade de nossas equipes de pesquisa, produto e consultoria, juntamente com parceiros externos, como a Hugging Face, ajudamos a trazer o poder dos modelos de base para nossos clientes e a criar IAs confi\u00e1veis em qualquer empresa. A IBM tamb\u00e9m continua investindo na cria\u00e7\u00e3o de novas plataformas, como a IA IBM watsonx e plataformas e tecnologias de dados, para projetar e desenvolver modelos de IA para se comportar de maneira audit\u00e1vel e confi\u00e1vel.\n\nEste documento descreve o ponto de vista da IBM sobre a \u00e9tica dos modelos de base. \u00c9 a primeira vers\u00e3o, e as vers\u00f5es futuras expandir\u00e3o v\u00e1rios aspectos da abordagem \u00e9tica do modelo de base da IBM. Esperamos que este documento seja \u00fatil para todos os stakeholders no desenvolvimento, implementa\u00e7\u00e3o e uso do modelo de base de forma respons\u00e1vel.\n\n\n\n## Benef\u00edcios dos modelos de base\n\nOs modelos de base podem melhorar significativamente o processo de desenvolvimento de sistemas de IA e auxiliar no avan\u00e7o da IA da fase de explora\u00e7\u00e3o para a ado\u00e7\u00e3o nas empresas. Seus benef\u00edcios incluem:\n\n## Realizar tarefas complexas\n\nModelos de base mostram um aumento significativo no desempenho na resolu\u00e7\u00e3o de problemas complexos e dif\u00edceis. Por exemplo, o modelo de base geoespacial da colabora\u00e7\u00e3o IBM e NASA foi projetado para converter os dados de sat\u00e9lite da NASA em mapas de desastres naturais, como inunda\u00e7\u00f5es e outras mudan\u00e7as de cen\u00e1rio. O modelo tamb\u00e9m pode ser usado para ajudar a revelar o\u00a0passado do nosso planeta; estimar riscos para culturas, empresas ou infraestruturas devido ao clima severo; desenvolver estrat\u00e9gias para se adaptar \u00e0s mudan\u00e7as clim\u00e1ticas; e auxiliar no agroneg\u00f3cio. O modelo est\u00e1 planejado para ser disponibilizado previamente aos clientes IBM por meio do IBM Environmental Intelligence Suite.\n\nPara ilustrar, o MoLFormer-XL da IBM \u00e9 um modelo de base que \u00e9\u00a0capaz de inferir a estrutura de mol\u00e9culas a partir de representa\u00e7\u00f5es simples, tornando mais f\u00e1cil a aprendizagem de v\u00e1rias tarefas de recebimento de dados, como prever as propriedades f\u00edsicas e\u00a0qu\u00e2nticas de uma mol\u00e9cula, identificar mol\u00e9culas semelhantes, rastrear mol\u00e9culas j\u00e1 aprovadas para novos casos de uso e descobrir novas mol\u00e9culas. Moderna e IBM est\u00e3o explorando formas de usar o MoLExer para ajudar a prever propriedades das mol\u00e9culas e entender as caracter\u00edsticas de poss\u00edveis medicamentos de mRNA.\n\n## Maior produtividade\n\nA natureza generativa dos modelos de base amplia o n\u00famero de \u00e1reas em que a IA pode ser usada em uma empresa para ajudar a melhorar a\u00a0produtividade, automatizando tarefas rotineiras e tediosas e\u00a0permitindo que os usu\u00e1rios dediquem mais tempo ao trabalho criativo e inovador. Por exemplo, o IBM Watsonx Code Assistant, desenvolvido com modelos de base, possibilita que desenvolvedores, independentemente do n\u00edvel de experi\u00eancia, escrevam c\u00f3digos usando recomenda\u00e7\u00f5es geradas por IA.\n\n## Time to value mais r\u00e1pido\n\nModelos de base geralmente s\u00e3o treinados com dados n\u00e3o rotulados, que est\u00e3o mais dispon\u00edveis em grandes quantidades do que dados rotulados. Uma vez treinados, os modelos de base podem ser usados diretamente ou ap\u00f3s serem ajustados para aplicativos de recebimento de dados, usando uma pequena quantidade de dados rotulados especializados, que podem diminuir a cria\u00e7\u00e3o do time to value.\n\n\n\n## Utilize diversas modalidades de dados\n\nOs modelos de base podem ser treinados usando diversas modalidades de dados, como l\u00edngua natural, texto, imagem e \u00e1udio. Eles tamb\u00e9m podem ser aplicados a tarefas que exigem diferentes tipos de dados, como dados de s\u00e9ries temporais, dados geoespaciais, dados tabulares, dados semiestruturados e dados de modalidade mista, como texto combinado com imagens.\n\n## Despesas amortizadas\n\nEmbora o custo inicial do treinamento de um modelo de base seja significativamente maior do que o treinamento de um modelo de IA tradicional, o custo adicional para aplic\u00e1-lo em uma nova tarefa \u00e9 consideravelmente menor. O uso de modelos de base pr\u00e9-treinados poderia ajudar a eliminar a necessidade de que as empresas fa\u00e7am investimentos substanciais para treinar modelos de base e explorar suas novas capacidades. Para uma empresa, a confiabilidade dos modelos, a\u00a0efici\u00eancia energ\u00e9tica, o desempenho, a portabilidade e a capacidade de\u00a0usar dados corporativos de forma eficaz e segura s\u00e3o fundamentais.\n\nA IBM permite que as empresas criem e detenham o valor de modelos de base para seus neg\u00f3cios, trazendo as melhores inova\u00e7\u00f5es da comunidade de IA aberta e global, operando de forma eficiente em ambientes de computa\u00e7\u00e3o h\u00edbrida, ajudando a mitigar riscos e controlando rigorosamente a IA.\n\n\n\n## Riscos dos modelos de base\n\nComo todas as tecnologias que avan\u00e7am rapidamente, os modelos de base oferecem riscos e benef\u00edcios. Alguns s\u00e3o riscos legais, como restri\u00e7\u00f5es \u00e0 movimenta\u00e7\u00e3o ou uso de dados, e precisam ser cuidadosamente avaliados de acordo com a legisla\u00e7\u00e3o atual e em evolu\u00e7\u00e3o. Outros riscos t\u00eam uma natureza \u00e9tica e devem ser considerados cuidadosamente para que a tecnologia tenha um impacto positivo. Em geral, os riscos de IA levantam quest\u00f5es sociot\u00e9cnicas e\u00a0devem ser abordados e mitigados por meio de m\u00e9todos sociot\u00e9cnicos, incluindo ferramentas de software, processos de avalia\u00e7\u00e3o de risco, frameworks de \u00e9tica em IA, mecanismos de controle, consultas multistakeholder, padr\u00f5es e regulamenta\u00e7\u00e3o. Iremos listar os riscos considerando as seguintes 3 categorias:\n\n- 1.  Tradicional. Riscos conhecidos de formas anteriores ou anteriores de\u00a0sistemas de IA\n- 2.  Amplificados. Riscos conhecidos, mas agora intensificados devido \u00e0s caracter\u00edsticas intr\u00ednsecas dos modelos de base, principalmente seus recursos generativos inerentes\n- 3.  Novo. Riscos emergentes intr\u00ednsecos aos modelos de base e suas capacidades generativas inerentes\n\nTamb\u00e9m estruturamos a lista de riscos em rela\u00e7\u00e3o a se est\u00e3o principalmente associados ao conte\u00fado fornecido ao modelo base, o\u00a0input, ou ao conte\u00fado gerado por ele, o output, ou se est\u00e3o relacionados a desafios adicionais.\n\n\n\n## 1. Riscos associados \u00e0 entrada\n\n## Fase de treinamento e ajuste\n\n\n\nGrupo\n\nRisco\n\n\n\n## Infer\u00eancia Fase\n\n\n\n## 2. Riscos associados \u00e0 sa\u00edda\n\n\n\n\n\n## 3. Desafios\n\n\n\n\n\n## Exemplos de risco\n\nN\u00f3s fornecemos exemplos cobertos pela imprensa para ajudar a\u00a0explicar muitos dos riscos dos modelos de base.\u00a0Muitos desses eventos cobertos pela imprensa ainda est\u00e3o em evolu\u00e7\u00e3o ou foram resolvidos, e fazer refer\u00eancia a eles pode ajudar o leitor a entender os riscos potenciais e trabalhar para mitig\u00e1-los.\u00a0Destacar esses exemplos \u00e9\u00a0apenas para fins ilustrativos.\n\n## Exemplos de risco: Input\n\n## Treinamento e ajuste Fase\n\n\n\n## Risco\n\n## Exemplo\n\n\n\n## Infer\u00eancia Fase\n\n\n\n## Exemplos de risco: Output\n\n\n\n\n\n\n\n## Exemplos de riscos: desafios\n\n\n\n## Exemplos de riscos: desafios\n\n\n\n## Princ\u00edpios, pilares e controle\n\n## Os Princ\u00edpios para Confian\u00e7a e Transpar\u00eancia da IBM e os Pilares\n\npara IA confi\u00e1vel s\u00e3o a base para as iniciativas de \u00e9tica em IA da IBM. A\u00a0IBM estabeleceu um Conselho de \u00c9tica em IA com a miss\u00e3o de apoiar um processo centralizado de controle, revis\u00e3o e tomada de decis\u00f5es para pol\u00edticas, pr\u00e1ticas, comunica\u00e7\u00f5es, pesquisa, produtos e servi\u00e7os de \u00e9tica em IA da IBM. O conselho inclui um conjunto diversificado de stakeholders de toda a empresa e \u00e9 apoiado por uma comunidade de funcion\u00e1rios da IBM que atuam como pontos focais de IA e defensores da \u00e9tica em IA. Por meio do conselho, os princ\u00edpios da IBM s\u00e3o colocados em pr\u00e1tica. Conforme novas tecnologias surgem, como modelos de base, o Conselho de \u00c9tica em IA da IBM est\u00e1 ativamente engajado em apoiar o alinhamento com esses Princ\u00edpios e Pilares, que evoluem para abordar novas quest\u00f5es \u00e9ticas em IA.\n\n\n\n## Prote\u00e7\u00f5es e mitiga\u00e7\u00f5es\n\nA IBM estabeleceu uma cultura organizacional que apoia o desenvolvimento e o uso respons\u00e1veis de IA. Conforme indicado no relat\u00f3rio de \u00e9tica em a\u00e7\u00e3o na IA do IBM Institute for Business Value, a \u00e9tica em IA j\u00e1 se tornou mais orientada pelos neg\u00f3cios do que pela tecnologia, e os executivos n\u00e3o t\u00e9cnicos agora s\u00e3o os principais defensores da \u00e9tica em IA, aumentando de 15% em 2018 para 80% 3\u00a0anos depois. Al\u00e9m disso, 79% dos CEOs est\u00e3o agora preparados para agir em quest\u00f5es \u00e9ticas de IA, contra 20%. Reconhecemos que a IA respons\u00e1vel \u00e9 uma \u00e1rea sociot\u00e9cnica que necessita de um investimento hol\u00edstico em cultura, processos e ferramentas. Nosso investimento em cultura organizacional pr\u00f3pria inclui a montagem de equipes inclusivas e multidisciplinares e o estabelecimento de processos e estruturas para\u00a0avaliar riscos.\n\nA IBM est\u00e1 engajada em pesquisa de ponta e desenvolvimento de ferramentas para ajudar os profissionais de suporte durante todo o\u00a0ciclo de vida da IA respons\u00e1vel e confi\u00e1vel. A plataforma de IA e dados empresariais watsonx, \u00e9 desenvolvida com 3 componentes: o IBM watsonx.ai\u2122 AI studio, o armazenamento de dados IBM watsonx.data\u2122 e\u00a0o kit de ferramentas IBM watsonx.governance\u2122. A tecnologia de controle de IA da IBM permite que os usu\u00e1rios promovam fluxos de trabalho de IA respons\u00e1veis, transparentes e explic\u00e1veis. Essa tecnologia inclui o IBM Watson OpenScale, que monitora e mede os resultados dos modelos de IA ao longo de seu ciclo de vida e auxilia as organiza\u00e7\u00f5es na supervis\u00e3o de aspectos como justi\u00e7a, explicabilidade, resili\u00eancia, alinhamento com resultados de neg\u00f3cios e conformidade. A\u00a0IBM tamb\u00e9m desenvolveu v\u00e1rios m\u00e9todos para ajudar com problemas de vi\u00e9s como FairIJ, Equi-tuning e FairReprogram. Leia mais sobre outras ferramentas de IA de software livre e confi\u00e1veis .\n\nAs prote\u00e7\u00f5es e mitiga\u00e7\u00f5es adicionais incluem:\n\n## Relat\u00f3rios de transpar\u00eancia\n\nUsar modelos de fichas t\u00e9cnicas padronizadas \u00e9 uma maneira de registrar com precis\u00e3o detalhes sobre os dados e modelos, prop\u00f3sito e\u00a0poss\u00edveis usos e riscos.\n\nLeia mais aqui \u2192\n\n## Filtragem de dados indesej\u00e1veis\n\nUsar dados de qualidade superior e selecionados pode ajudar a mitigar determinados problemas. A IBM est\u00e1 desenvolvendo t\u00e9cnicas de filtragem para ajudar a reduzir as chances de produzir conte\u00fado indesej\u00e1vel e desalinhado por remover linguagem de \u00f3dio, linguagem tendenciosa e profanidade dos dados.\n\nLeia mais aqui \u2192\n\n## Adapta\u00e7\u00e3o de dom\u00ednio\n\nTreinar um modelo de base para um dom\u00ednio ou setor espec\u00edfico pode ajudar a minimizar o escopo de risco para o qual os modelos podem dar\u00a0origem, pois ele pode ser condicionado a gerar resultados que s\u00e3o ajustados para serem mais relevantes para esse dom\u00ednio ou setor. Leia mais aqui \u2192\n\n\n\n## Supervis\u00e3o humana e an\u00e1lise humana no loop\n\nA supervis\u00e3o e revis\u00e3o humanas podem ajudar a identificar e corrigir erros e vieses no output gerado. Al\u00e9m disso, a valida\u00e7\u00e3o e o feedback humanos sobre a qualidade das respostas do modelo ajudam a garantir que o conte\u00fado gerado seja preciso, relevante, de alta qualidade, n\u00e3o\u00a0esteja divergindo e esteja alinhado.\n\nLeia mais aqui \u2192\n\n## Compromisso de consultoria\n\nA IBM Consulting se dedica a ajudar os clientes com o uso seguro e respons\u00e1vel da IA, independentemente do stack tecnol\u00f3gico preferido. Eles ajudam os clientes a cultivar uma cultura que adota e expande a IA com seguran\u00e7a, cria ferramentas de investiga\u00e7\u00e3o para ver dentro de algoritmos de caixa preta e garante que a estrat\u00e9gia corporativa dos clientes inclua princ\u00edpios s\u00f3lidos de governan\u00e7a de dados.\n\nLeia mais aqui \u2192\n\n## IBM Enterprise Design Thinking\n\nOs m\u00e9todos e estruturas IBM Enterprise Design Thinking, como o Team Essentials for AI, ajudam os clientes a definir comportamentos \u00e9ticos em\u00a0todo o processo de design e desenvolvimento de IA.\n\nLeia mais aqui \u2192\n\n## Revis\u00e3o \u00e9tica da IA\n\nAvalia\u00e7\u00e3o de capacidades, limita\u00e7\u00f5es e riscos em projetos de IA ajudam a garantir o desenvolvimento e uso respons\u00e1vel da tecnologia.\n\n## \u00c9tica por Design\n\nA \u00c9tica por Design \u00e9 um framework estruturado com o objetivo de integrar \u00e9tica tecnol\u00f3gica no pipeline de desenvolvimento de tecnologia, incluindo, entre outros, sistemas de IA. A \u00c9tica por Design viabiliza IA e outras tecnologias como uma for\u00e7a para o bem, incorporando princ\u00edpios de \u00e9tica tecnol\u00f3gica em produtos, servi\u00e7os e opera\u00e7\u00f5es mais amplas.\n\n## Diversidade na equipe\n\nA diversidade nas equipes que desenvolvem e treinam sistemas de IA, incluindo modelos de base, ajuda a garantir que uma variedade de perspectivas e experi\u00eancias sejam consideradas. Essa diversidade melhora a precis\u00e3o e o desempenho dos sistemas de IA e ajuda a reduzir os riscos ao longo do ciclo de vida de IA, incluindo o potencial para desfechos adversos que afetam grupos que podem n\u00e3o ser bem representados em equipes menos diversificadas.\n\n\n\n## Pol\u00edticas, regulamentos e melhores pr\u00e1ticas de\u00a0IA\n\nUm Guia dos Formuladores de Pol\u00edticas para Modelos de Base apresenta o que os formuladores de pol\u00edticas precisam saber sobre modelos de base. Este blog, do Laborat\u00f3rio de Pol\u00edticas da IBM, tem como objetivo ajudar os formuladores de pol\u00edticas na tarefa complexa de regular o\u00a0uso de IA generativa, visando evitar os riscos sem limitar a inova\u00e7\u00e3o e as oportunidades ben\u00e9ficas. Para obter mais informa\u00e7\u00f5es sobre as recomenda\u00e7\u00f5es da IBM aos formuladores de pol\u00edticas, leia o depoimento da Diretora de Privacidade e Confian\u00e7a da IBM, Christina Montgomery, diante da Subcomiss\u00e3o Judici\u00e1ria de Privacidade, Tecnologia e Lei do Senado dos EUA aqui.\n\nA IBM est\u00e1 causando um impacto na forma\u00e7\u00e3o de pol\u00edticas regulat\u00f3rias, melhores pr\u00e1ticas e ferramentas do setor, controle de tecnologias emergentes e pesquisa sociot\u00e9cnica, liderando e contribuindo para\u00a0iniciativas com organiza\u00e7\u00f5es como:\n\n- - O F\u00f3rum Econ\u00f4mico Mundial\n- - Parceria em IA\n- -Centro de controle de IA da Associa\u00e7\u00e3o Internacional de Profissionais de Privacidade (IAPP)\n- - Iniciativa global de IEEE sobre \u00e9tica de sistemas aut\u00f4nomos e inteligentes\n- -Participa\u00e7\u00e3o de Christina Montgomery do National Artificial Intelligence Advisory Committee (NAIAC)\n- - O Pacto Digital Global das Na\u00e7\u00f5es Unidas\n- -A Parceria Global em Intelig\u00eancia Artificial (GPAI)\n- - A Organiza\u00e7\u00e3o para Coopera\u00e7\u00e3o e Desenvolvimento Econ\u00f4mico (OECD)\n- - A Data & Trust Alliance\n\nA IBM tem parcerias acad\u00eamicas s\u00f3lidas, como o MIT-IBM Watson AI\u00a0Lab, onde uma comunidade de cientistas do MIT e da IBM Research conduzem pesquisas sobre IA e trabalham com organiza\u00e7\u00f5es globais para unir algoritmos ao seu impacto nos neg\u00f3cios e na sociedade. O\u00a0Notre Dame-IBM Tech Ethics Lab foi formado para abordar as diversas quest\u00f5es \u00e9ticas implicadas pelo desenvolvimento e uso de tecnologias avan\u00e7adas, incluindo IA, aprendizado de m\u00e1quina (ML) e computa\u00e7\u00e3o qu\u00e2ntica. A pesquisa de Intelig\u00eancia Artificial Centrada no Homem (HAI) da Universidade de Stanford promove pesquisas, educa\u00e7\u00e3o, pol\u00edticas e\u00a0pr\u00e1ticas de IA.\n\n\n\nContinue acompanhando este espa\u00e7o para obter mais informa\u00e7\u00f5es sobre os \u00faltimos avan\u00e7os em modelos de base e como a IBM est\u00e1 trabalhando para o desenvolvimento respons\u00e1vel e uso desta e de outras tecnologias.\n\n\n\n\u00a9 Copyright IBM Corporation 2023, 2024\n\nIBM Brasil Ltda Rua Tut\u00f3ia, 1157 CEP 04007-900 S\u00e3o Paulo, SP IBM Corporation New Orchard Road Armonk, NY 10504\n\nProduzido nos Estados Unidos da Am\u00e9rica Fevereiro de 2024\n\nIBM, o logotipo da IBM, Enterprise Design Thinking, IBM Consulting, IBM Research, IBM Watson, watsonx, watsonx.ai, watsonx.data e watsonx.governance s\u00e3o marcas comerciais ou marcas registradas da International Business Machines Corporation, nos Estados Unidos e/ou em outros pa\u00edses. Outros nomes de produtos e servi\u00e7os podem ser marcas comerciais da IBM ou de outras empresas. Uma lista atual de marcas comerciais da IBM est\u00e1 dispon\u00edvel em ibm.com/br-pt/trademark.\n\nEste documento \u00e9 atual na data de sua publica\u00e7\u00e3o inicial, podendo ser alterado pela IBM a qualquer momento. Nem todas as ofertas est\u00e3o dispon\u00edveis em todos os pa\u00edses nos quais a IBM opera.\n\nAS INFORMA\u00c7\u00d5ES CONTIDAS NESTE DOCUMENTO S\u00c3O FORNECIDAS NO ESTADO EM QUE SEM ENCONTRAM, SEM QUALQUER GARANTIA, EXPRESSA OU IMPL\u00cdCITA, INCLUSIVE SEM QUALQUER GARANTIA DE COMERCIALIZA\u00c7\u00c3O, ADEQUA\u00c7\u00c3O A DETERMINADO FIM E QUALQUER GARANTIA OU CONDI\u00c7\u00c3O DE N\u00c3O INFRA\u00c7\u00c3O. Os produtos IBM t\u00eam a garantia prevista nos termos e condi\u00e7\u00f5es dos contratos sob os quais s\u00e3o fornecidos.\n\nDeclara\u00e7\u00e3o de boas pr\u00e1ticas de seguran\u00e7a: nenhum sistema ou produto de TI deve ser considerado completamente seguro, e nenhuma medida exclusiva de produto, servi\u00e7o ou seguran\u00e7a pode ser completamente eficaz na preven\u00e7\u00e3o de uso ou acesso inadequado. A IBM n\u00e3o garante que nenhum de seus sistemas, produtos ou servi\u00e7os estejam imunes nem que tornar\u00e3o sua empresa imune a condutas maliciosas ou ilegais por parte de terceiros.\n\nO cliente \u00e9 respons\u00e1vel por garantir o cumprimento de todas as leis e regulamentos aplic\u00e1veis. A IBM n\u00e3o fornece conselho jur\u00eddico tampouco representa ou garante que seus servi\u00e7os ou produtos garantir\u00e3o que o cliente esteja em conformidade com qualquer lei ou regulamenta\u00e7\u00e3o. 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"orig": "Princ\u00edpios, pilares e governan\u00e7a", "text": "Princ\u00edpios, pilares e governan\u00e7a", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/20", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 175.0, "t": 416.752, "r": 203.8, "b": 396.492, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "25", "text": "25", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/21", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 175.0, "t": 381.584, "r": 220.288, "b": 362.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Prote\u00e7\u00f5es e mitiga\u00e7\u00f5es", "text": "Prote\u00e7\u00f5es e mitiga\u00e7\u00f5es", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/22", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 175.0, "t": 332.752, "r": 203.8, "b": 312.492, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/23", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 175.0, "t": 297.584, "r": 273.471, "b": 266.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "Pol\u00edticas, regulamenta\u00e7\u00f5es e\u00a0melhores pr\u00e1ticas de IA Exemplos", "text": "Pol\u00edticas, regulamenta\u00e7\u00f5es e\u00a0melhores pr\u00e1ticas de IA Exemplos", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/24", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 3, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 36.2, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/25", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 32.0, "t": 674.032, "r": 225.894, "b": 653.772, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Resumo executivo", "text": "Resumo executivo", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/26", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 32.0, "t": 417.584, "r": 287.237, "b": 266.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 849]}], "orig": "A ascens\u00e3o dos modelos de base oferece \u00e0s empresas novas e empolgantes possibilidades, mas tamb\u00e9m levanta quest\u00f5es novas e amplas sobre design, desenvolvimento, implementa\u00e7\u00e3o e uso \u00e9tico. Segundo uma recente pesquisa sobre IA generativa do IBM Institute for Business Value , as organiza\u00e7\u00f5es j\u00e1 est\u00e3o manifestando preocupa\u00e7\u00f5es sobre quest\u00f5es relacionadas \u00e0 confian\u00e7a, especificamente como barreiras para investimentos. Suas principais preocupa\u00e7\u00f5es s\u00e3o ciberseguran\u00e7a (57%), privacidade (51%) e precis\u00e3o (47%). Muitas organiza\u00e7\u00f5es estavam levando essas preocupa\u00e7\u00f5es a s\u00e9rio antes da 'consumeriza\u00e7\u00e3o' da IA generativa, expressando sua inten\u00e7\u00e3o de investir pelo menos 40% mais em \u00e9tica de IA nos pr\u00f3ximos tr\u00eas anos. A\u00a0conscientiza\u00e7\u00e3o sobre riscos e poss\u00edveis maneiras de mitig\u00e1-los \u00e9 o primeiro passo crucial para a cria\u00e7\u00e3o de sistemas de IA confi\u00e1veis.", "text": "A ascens\u00e3o dos modelos de base oferece \u00e0s empresas novas e empolgantes possibilidades, mas tamb\u00e9m levanta quest\u00f5es novas e amplas sobre design, desenvolvimento, implementa\u00e7\u00e3o e uso \u00e9tico. Segundo uma recente pesquisa sobre IA generativa do IBM Institute for Business Value , as organiza\u00e7\u00f5es j\u00e1 est\u00e3o manifestando preocupa\u00e7\u00f5es sobre quest\u00f5es relacionadas \u00e0 confian\u00e7a, especificamente como barreiras para investimentos. Suas principais preocupa\u00e7\u00f5es s\u00e3o ciberseguran\u00e7a (57%), privacidade (51%) e precis\u00e3o (47%). Muitas organiza\u00e7\u00f5es estavam levando essas preocupa\u00e7\u00f5es a s\u00e9rio antes da 'consumeriza\u00e7\u00e3o' da IA generativa, expressando sua inten\u00e7\u00e3o de investir pelo menos 40% mais em \u00e9tica de IA nos pr\u00f3ximos tr\u00eas anos. A\u00a0conscientiza\u00e7\u00e3o sobre riscos e poss\u00edveis maneiras de mitig\u00e1-los \u00e9 o primeiro passo crucial para a cria\u00e7\u00e3o de sistemas de IA confi\u00e1veis.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/27", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 318.0, "t": 417.584, "r": 384.143, "b": 410.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Neste documento:", "text": "Neste documento:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/28", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 365.496, "t": 381.584, "r": 580.638, "b": 326.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 234]}], "orig": "Exploraremos as vantagens dos modelos de base, incluindo sua capacidade de realizar tarefas desafiadoras, potencial para acelerar a ado\u00e7\u00e3o de IA, habilidade de aumentar a produtividade e os benef\u00edcios econ\u00f4micos que eles proporcionam.", "text": "Exploraremos as vantagens dos modelos de base, incluindo sua capacidade de realizar tarefas desafiadoras, potencial para acelerar a ado\u00e7\u00e3o de IA, habilidade de aumentar a produtividade e os benef\u00edcios econ\u00f4micos que eles proporcionam.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/29", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 365.496, "t": 297.584, "r": 577.83, "b": 254.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 226]}], "orig": "Discutiremos as tr\u00eas categorias de risco, incluindo riscos conhecidos de formas anteriores de IA, riscos conhecidos amplificados por modelos de base e riscos emergentes intr\u00ednsecos aos recursos generativos dos modelos de base.", "text": "Discutiremos as tr\u00eas categorias de risco, incluindo riscos conhecidos de formas anteriores de IA, riscos conhecidos amplificados por modelos de base e riscos emergentes intr\u00ednsecos aos recursos generativos dos modelos de base.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/30", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 365.496, "t": 225.58400000000006, "r": 561.702, "b": 194.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 156]}], "orig": "Abordaremos os princ\u00edpios, os pilares e o controle que formam a base das iniciativas \u00e9ticas de IA da IBM e sugeriremos barreiras para a mitiga\u00e7\u00e3o de riscos.", "text": "Abordaremos os princ\u00edpios, os pilares e o controle que formam a base das iniciativas \u00e9ticas de IA da IBM e sugeriremos barreiras para a mitiga\u00e7\u00e3o de riscos.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/31", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 36.2, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/32", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 318.0, "t": 36.88599999999997, "r": 542.943, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/33", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 5, "bbox": {"l": 32.0, "t": 674.032, "r": 149.648, "b": 653.772, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Introdu\u00e7\u00e3o", "text": "Introdu\u00e7\u00e3o", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/34", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 32.0, "t": 417.584, "r": 295.888, "b": 242.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 966]}], "orig": "\u00c0 medida que o uso de IA continua se expandindo, os grandes e complexos modelos de IA est\u00e3o fornecendo resultados promissores de desempenho, bem como resolvendo alguns dos problemas mais desafiadores da sociedade. No entanto, criar grandes conjuntos de dados de treinamento e modelos complexos para cada aplicativo de IA pode ser extremamente dif\u00edcil para as empresas. Modelos de base fornecem um caminho para alcan\u00e7ar o melhor dos dois mundos: desenvolver modelos de \u00faltima gera\u00e7\u00e3o poderosos e reutiliz\u00e1-los diretamente ou aplicar m\u00e9todos de ajuste para implementar uma variedade de casos de uso, em vez de treinar novos modelos para cada caso de uso. Por exemplo, a IBM Research desenvolveu modelos de base para inspe\u00e7\u00e3o visual. Esses modelos de base aprendem a representa\u00e7\u00e3o geral de superf\u00edcies e corredores de concreto e podem ser ajustados ainda mais para casos de uso espec\u00edficos, como detec\u00e7\u00e3o de rachaduras ou inspe\u00e7\u00e3o de defeitos com dados menos rotulados.", "text": "\u00c0 medida que o uso de IA continua se expandindo, os grandes e complexos modelos de IA est\u00e3o fornecendo resultados promissores de desempenho, bem como resolvendo alguns dos problemas mais desafiadores da sociedade. No entanto, criar grandes conjuntos de dados de treinamento e modelos complexos para cada aplicativo de IA pode ser extremamente dif\u00edcil para as empresas. Modelos de base fornecem um caminho para alcan\u00e7ar o melhor dos dois mundos: desenvolver modelos de \u00faltima gera\u00e7\u00e3o poderosos e reutiliz\u00e1-los diretamente ou aplicar m\u00e9todos de ajuste para implementar uma variedade de casos de uso, em vez de treinar novos modelos para cada caso de uso. Por exemplo, a IBM Research desenvolveu modelos de base para inspe\u00e7\u00e3o visual. Esses modelos de base aprendem a representa\u00e7\u00e3o geral de superf\u00edcies e corredores de concreto e podem ser ajustados ainda mais para casos de uso espec\u00edficos, como detec\u00e7\u00e3o de rachaduras ou inspe\u00e7\u00e3o de defeitos com dados menos rotulados.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/35", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 32.0, "t": 225.58400000000006, "r": 285.718, "b": 158.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 342]}], "orig": "A IBM define um modelo de base como um modelo de IA que pode ser adaptado a uma ampla gama de tarefas de recebimento de dados. Os\u00a0modelos de base normalmente s\u00e3o modelos generativos de grande escala treinados em dados n\u00e3o rotulados usando autossupervis\u00e3o. Como\u00a0modelos de grande escala, os modelos de base podem incluir bilh\u00f5es de par\u00e2metros.", "text": "A IBM define um modelo de base como um modelo de IA que pode ser adaptado a uma ampla gama de tarefas de recebimento de dados. Os\u00a0modelos de base normalmente s\u00e3o modelos generativos de grande escala treinados em dados n\u00e3o rotulados usando autossupervis\u00e3o. Como\u00a0modelos de grande escala, os modelos de base podem incluir bilh\u00f5es de par\u00e2metros.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/36", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 318.0, "t": 417.584, "r": 578.005, "b": 314.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 599]}], "orig": "A IBM \u00e9 uma empresa de nuvem h\u00edbrida e IA com vasta reputa\u00e7\u00e3o como administradora de dados respons\u00e1vel e comprometida com a \u00e9tica em IA. Usando a capacidade de nossas equipes de pesquisa, produto e consultoria, juntamente com parceiros externos, como a Hugging Face, ajudamos a trazer o poder dos modelos de base para nossos clientes e a criar IAs confi\u00e1veis em qualquer empresa. A IBM tamb\u00e9m continua investindo na cria\u00e7\u00e3o de novas plataformas, como a IA IBM watsonx e plataformas e tecnologias de dados, para projetar e desenvolver modelos de IA para se comportar de maneira audit\u00e1vel e confi\u00e1vel.", "text": "A IBM \u00e9 uma empresa de nuvem h\u00edbrida e IA com vasta reputa\u00e7\u00e3o como administradora de dados respons\u00e1vel e comprometida com a \u00e9tica em IA. Usando a capacidade de nossas equipes de pesquisa, produto e consultoria, juntamente com parceiros externos, como a Hugging Face, ajudamos a trazer o poder dos modelos de base para nossos clientes e a criar IAs confi\u00e1veis em qualquer empresa. A IBM tamb\u00e9m continua investindo na cria\u00e7\u00e3o de novas plataformas, como a IA IBM watsonx e plataformas e tecnologias de dados, para projetar e desenvolver modelos de IA para se comportar de maneira audit\u00e1vel e confi\u00e1vel.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 318.0, "t": 297.584, "r": 575.038, "b": 230.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 342]}], "orig": "Este documento descreve o ponto de vista da IBM sobre a \u00e9tica dos modelos de base. \u00c9 a primeira vers\u00e3o, e as vers\u00f5es futuras expandir\u00e3o v\u00e1rios aspectos da abordagem \u00e9tica do modelo de base da IBM. Esperamos que este documento seja \u00fatil para todos os stakeholders no desenvolvimento, implementa\u00e7\u00e3o e uso do modelo de base de forma respons\u00e1vel.", "text": "Este documento descreve o ponto de vista da IBM sobre a \u00e9tica dos modelos de base. \u00c9 a primeira vers\u00e3o, e as vers\u00f5es futuras expandir\u00e3o v\u00e1rios aspectos da abordagem \u00e9tica do modelo de base da IBM. Esperamos que este documento seja \u00fatil para todos os stakeholders no desenvolvimento, implementa\u00e7\u00e3o e uso do modelo de base de forma respons\u00e1vel.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 36.2, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 318.0, "t": 36.88599999999997, "r": 542.943, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 32.0, "t": 674.032, "r": 213.056, "b": 623.772, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Benef\u00edcios dos modelos de base", "text": "Benef\u00edcios dos modelos de base", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 32.0, "t": 417.584, "r": 293.59, "b": 386.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 202]}], "orig": "Os modelos de base podem melhorar significativamente o processo de desenvolvimento de sistemas de IA e auxiliar no avan\u00e7o da IA da fase de explora\u00e7\u00e3o para a ado\u00e7\u00e3o nas empresas. Seus benef\u00edcios incluem:", "text": "Os modelos de base podem melhorar significativamente o processo de desenvolvimento de sistemas de IA e auxiliar no avan\u00e7o da IA da fase de explora\u00e7\u00e3o para a ado\u00e7\u00e3o nas empresas. Seus benef\u00edcios incluem:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 32.0, "t": 369.584, "r": 132.374, "b": 362.698, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "Realizar tarefas complexas", "text": "Realizar tarefas complexas", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 32.0, "t": 357.584, "r": 280.158, "b": 230.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 696]}], "orig": "Modelos de base mostram um aumento significativo no desempenho na resolu\u00e7\u00e3o de problemas complexos e dif\u00edceis. Por exemplo, o modelo de base geoespacial da colabora\u00e7\u00e3o IBM e NASA foi projetado para converter os dados de sat\u00e9lite da NASA em mapas de desastres naturais, como inunda\u00e7\u00f5es e outras mudan\u00e7as de cen\u00e1rio. O modelo tamb\u00e9m pode ser usado para ajudar a revelar o\u00a0passado do nosso planeta; estimar riscos para culturas, empresas ou infraestruturas devido ao clima severo; desenvolver estrat\u00e9gias para se adaptar \u00e0s mudan\u00e7as clim\u00e1ticas; e auxiliar no agroneg\u00f3cio. O modelo est\u00e1 planejado para ser disponibilizado previamente aos clientes IBM por meio do IBM Environmental Intelligence Suite.", "text": "Modelos de base mostram um aumento significativo no desempenho na resolu\u00e7\u00e3o de problemas complexos e dif\u00edceis. Por exemplo, o modelo de base geoespacial da colabora\u00e7\u00e3o IBM e NASA foi projetado para converter os dados de sat\u00e9lite da NASA em mapas de desastres naturais, como inunda\u00e7\u00f5es e outras mudan\u00e7as de cen\u00e1rio. O modelo tamb\u00e9m pode ser usado para ajudar a revelar o\u00a0passado do nosso planeta; estimar riscos para culturas, empresas ou infraestruturas devido ao clima severo; desenvolver estrat\u00e9gias para se adaptar \u00e0s mudan\u00e7as clim\u00e1ticas; e auxiliar no agroneg\u00f3cio. O modelo est\u00e1 planejado para ser disponibilizado previamente aos clientes IBM por meio do IBM Environmental Intelligence Suite.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 32.0, "t": 213.58400000000006, "r": 282.461, "b": 110.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 572]}], "orig": "Para ilustrar, o MoLFormer-XL da IBM \u00e9 um modelo de base que \u00e9\u00a0capaz de inferir a estrutura de mol\u00e9culas a partir de representa\u00e7\u00f5es simples, tornando mais f\u00e1cil a aprendizagem de v\u00e1rias tarefas de recebimento de dados, como prever as propriedades f\u00edsicas e\u00a0qu\u00e2nticas de uma mol\u00e9cula, identificar mol\u00e9culas semelhantes, rastrear mol\u00e9culas j\u00e1 aprovadas para novos casos de uso e descobrir novas mol\u00e9culas. Moderna e IBM est\u00e3o explorando formas de usar o MoLExer para ajudar a prever propriedades das mol\u00e9culas e entender as caracter\u00edsticas de poss\u00edveis medicamentos de mRNA.", "text": "Para ilustrar, o MoLFormer-XL da IBM \u00e9 um modelo de base que \u00e9\u00a0capaz de inferir a estrutura de mol\u00e9culas a partir de representa\u00e7\u00f5es simples, tornando mais f\u00e1cil a aprendizagem de v\u00e1rias tarefas de recebimento de dados, como prever as propriedades f\u00edsicas e\u00a0qu\u00e2nticas de uma mol\u00e9cula, identificar mol\u00e9culas semelhantes, rastrear mol\u00e9culas j\u00e1 aprovadas para novos casos de uso e descobrir novas mol\u00e9culas. Moderna e IBM est\u00e3o explorando formas de usar o MoLExer para ajudar a prever propriedades das mol\u00e9culas e entender as caracter\u00edsticas de poss\u00edveis medicamentos de mRNA.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 318.0, "t": 417.584, "r": 392.791, "b": 410.698, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Maior produtividade", "text": "Maior produtividade", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 318.0, "t": 405.584, "r": 573.71, "b": 314.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 481]}], "orig": "A natureza generativa dos modelos de base amplia o n\u00famero de \u00e1reas em que a IA pode ser usada em uma empresa para ajudar a melhorar a\u00a0produtividade, automatizando tarefas rotineiras e tediosas e\u00a0permitindo que os usu\u00e1rios dediquem mais tempo ao trabalho criativo e inovador. Por exemplo, o IBM Watsonx Code Assistant, desenvolvido com modelos de base, possibilita que desenvolvedores, independentemente do n\u00edvel de experi\u00eancia, escrevam c\u00f3digos usando recomenda\u00e7\u00f5es geradas por IA.", "text": "A natureza generativa dos modelos de base amplia o n\u00famero de \u00e1reas em que a IA pode ser usada em uma empresa para ajudar a melhorar a\u00a0produtividade, automatizando tarefas rotineiras e tediosas e\u00a0permitindo que os usu\u00e1rios dediquem mais tempo ao trabalho criativo e inovador. Por exemplo, o IBM Watsonx Code Assistant, desenvolvido com modelos de base, possibilita que desenvolvedores, independentemente do n\u00edvel de experi\u00eancia, escrevam c\u00f3digos usando recomenda\u00e7\u00f5es geradas por IA.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 318.0, "t": 297.584, "r": 413.158, "b": 290.698, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "Time to value mais r\u00e1pido", "text": "Time to value mais r\u00e1pido", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/48", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 318.0, "t": 285.584, "r": 571.742, "b": 218.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 384]}], "orig": "Modelos de base geralmente s\u00e3o treinados com dados n\u00e3o rotulados, que est\u00e3o mais dispon\u00edveis em grandes quantidades do que dados rotulados. Uma vez treinados, os modelos de base podem ser usados diretamente ou ap\u00f3s serem ajustados para aplicativos de recebimento de dados, usando uma pequena quantidade de dados rotulados especializados, que podem diminuir a cria\u00e7\u00e3o do time to value.", "text": "Modelos de base geralmente s\u00e3o treinados com dados n\u00e3o rotulados, que est\u00e3o mais dispon\u00edveis em grandes quantidades do que dados rotulados. Uma vez treinados, os modelos de base podem ser usados diretamente ou ap\u00f3s serem ajustados para aplicativos de recebimento de dados, usando uma pequena quantidade de dados rotulados especializados, que podem diminuir a cria\u00e7\u00e3o do time to value.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 36.2, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 318.0, "t": 36.88599999999997, "r": 542.943, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/51", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 32.0, "t": 405.584, "r": 174.223, "b": 398.698, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Utilize diversas modalidades de dados", "text": "Utilize diversas modalidades de dados", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/52", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 32.0, "t": 393.584, "r": 290.678, "b": 326.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 355]}], "orig": "Os modelos de base podem ser treinados usando diversas modalidades de dados, como l\u00edngua natural, texto, imagem e \u00e1udio. Eles tamb\u00e9m podem ser aplicados a tarefas que exigem diferentes tipos de dados, como dados de s\u00e9ries temporais, dados geoespaciais, dados tabulares, dados semiestruturados e dados de modalidade mista, como texto combinado com imagens.", "text": "Os modelos de base podem ser treinados usando diversas modalidades de dados, como l\u00edngua natural, texto, imagem e \u00e1udio. Eles tamb\u00e9m podem ser aplicados a tarefas que exigem diferentes tipos de dados, como dados de s\u00e9ries temporais, dados geoespaciais, dados tabulares, dados semiestruturados e dados de modalidade mista, como texto combinado com imagens.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/53", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 32.0, "t": 309.584, "r": 115.783, "b": 302.698, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Despesas amortizadas", "text": "Despesas amortizadas", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 32.0, "t": 297.584, "r": 294.054, "b": 194.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 603]}], "orig": "Embora o custo inicial do treinamento de um modelo de base seja significativamente maior do que o treinamento de um modelo de IA tradicional, o custo adicional para aplic\u00e1-lo em uma nova tarefa \u00e9 consideravelmente menor. O uso de modelos de base pr\u00e9-treinados poderia ajudar a eliminar a necessidade de que as empresas fa\u00e7am investimentos substanciais para treinar modelos de base e explorar suas novas capacidades. Para uma empresa, a confiabilidade dos modelos, a\u00a0efici\u00eancia energ\u00e9tica, o desempenho, a portabilidade e a capacidade de\u00a0usar dados corporativos de forma eficaz e segura s\u00e3o fundamentais.", "text": "Embora o custo inicial do treinamento de um modelo de base seja significativamente maior do que o treinamento de um modelo de IA tradicional, o custo adicional para aplic\u00e1-lo em uma nova tarefa \u00e9 consideravelmente menor. O uso de modelos de base pr\u00e9-treinados poderia ajudar a eliminar a necessidade de que as empresas fa\u00e7am investimentos substanciais para treinar modelos de base e explorar suas novas capacidades. Para uma empresa, a confiabilidade dos modelos, a\u00a0efici\u00eancia energ\u00e9tica, o desempenho, a portabilidade e a capacidade de\u00a0usar dados corporativos de forma eficaz e segura s\u00e3o fundamentais.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 389.5, "t": 420.376, "r": 577.456, "b": 266.20000000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "A IBM permite que as empresas criem e detenham o valor de modelos de base para seus neg\u00f3cios, trazendo as melhores inova\u00e7\u00f5es da comunidade de IA aberta e global, operando de forma eficiente em ambientes de computa\u00e7\u00e3o h\u00edbrida, ajudando a mitigar riscos e controlando rigorosamente a IA.", "text": "A IBM permite que as empresas criem e detenham o valor de modelos de base para seus neg\u00f3cios, trazendo as melhores inova\u00e7\u00f5es da comunidade de IA aberta e global, operando de forma eficiente em ambientes de computa\u00e7\u00e3o h\u00edbrida, ajudando a mitigar riscos e controlando rigorosamente a IA.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 36.2, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 318.0, "t": 36.88599999999997, "r": 542.943, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/58", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 32.0, "t": 674.032, "r": 213.056, "b": 623.772, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "Riscos dos modelos de base", "text": "Riscos dos modelos de base", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 32.0, "t": 417.584, "r": 292.173, "b": 278.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 751]}], "orig": "Como todas as tecnologias que avan\u00e7am rapidamente, os modelos de base oferecem riscos e benef\u00edcios. Alguns s\u00e3o riscos legais, como restri\u00e7\u00f5es \u00e0 movimenta\u00e7\u00e3o ou uso de dados, e precisam ser cuidadosamente avaliados de acordo com a legisla\u00e7\u00e3o atual e em evolu\u00e7\u00e3o. Outros riscos t\u00eam uma natureza \u00e9tica e devem ser considerados cuidadosamente para que a tecnologia tenha um impacto positivo. Em geral, os riscos de IA levantam quest\u00f5es sociot\u00e9cnicas e\u00a0devem ser abordados e mitigados por meio de m\u00e9todos sociot\u00e9cnicos, incluindo ferramentas de software, processos de avalia\u00e7\u00e3o de risco, frameworks de \u00e9tica em IA, mecanismos de controle, consultas multistakeholder, padr\u00f5es e regulamenta\u00e7\u00e3o. Iremos listar os riscos considerando as seguintes 3 categorias:", "text": "Como todas as tecnologias que avan\u00e7am rapidamente, os modelos de base oferecem riscos e benef\u00edcios. Alguns s\u00e3o riscos legais, como restri\u00e7\u00f5es \u00e0 movimenta\u00e7\u00e3o ou uso de dados, e precisam ser cuidadosamente avaliados de acordo com a legisla\u00e7\u00e3o atual e em evolu\u00e7\u00e3o. Outros riscos t\u00eam uma natureza \u00e9tica e devem ser considerados cuidadosamente para que a tecnologia tenha um impacto positivo. Em geral, os riscos de IA levantam quest\u00f5es sociot\u00e9cnicas e\u00a0devem ser abordados e mitigados por meio de m\u00e9todos sociot\u00e9cnicos, incluindo ferramentas de software, processos de avalia\u00e7\u00e3o de risco, frameworks de \u00e9tica em IA, mecanismos de controle, consultas multistakeholder, padr\u00f5es e regulamenta\u00e7\u00e3o. Iremos listar os riscos considerando as seguintes 3 categorias:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/60", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 32.0, "t": 261.58400000000006, "r": 283.421, "b": 242.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 87]}], "orig": "1.  Tradicional. Riscos conhecidos de formas anteriores ou anteriores de\u00a0sistemas de IA", "text": "1.  Tradicional. Riscos conhecidos de formas anteriores ou anteriores de\u00a0sistemas de IA", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 32.0, "t": 237.58400000000006, "r": 294.214, "b": 206.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 171]}], "orig": "2.  Amplificados. Riscos conhecidos, mas agora intensificados devido \u00e0s caracter\u00edsticas intr\u00ednsecas dos modelos de base, principalmente seus recursos generativos inerentes", "text": "2.  Amplificados. Riscos conhecidos, mas agora intensificados devido \u00e0s caracter\u00edsticas intr\u00ednsecas dos modelos de base, principalmente seus recursos generativos inerentes", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 32.0, "t": 201.58400000000006, "r": 280.423, "b": 182.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 100]}], "orig": "3.  Novo. Riscos emergentes intr\u00ednsecos aos modelos de base e suas capacidades generativas inerentes", "text": "3.  Novo. Riscos emergentes intr\u00ednsecos aos modelos de base e suas capacidades generativas inerentes", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/63", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 32.0, "t": 165.58400000000006, "r": 271.926, "b": 122.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "Tamb\u00e9m estruturamos a lista de riscos em rela\u00e7\u00e3o a se est\u00e3o principalmente associados ao conte\u00fado fornecido ao modelo base, o\u00a0input, ou ao conte\u00fado gerado por ele, o output, ou se est\u00e3o relacionados a desafios adicionais.", "text": "Tamb\u00e9m estruturamos a lista de riscos em rela\u00e7\u00e3o a se est\u00e3o principalmente associados ao conte\u00fado fornecido ao modelo base, o\u00a0input, ou ao conte\u00fado gerado por ele, o output, ou se est\u00e3o relacionados a desafios adicionais.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 36.2, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "8", "text": "8", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 318.0, "t": 36.88599999999997, "r": 542.943, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 32.0, "t": 753.772, "r": 224.163, "b": 741.86, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "1. Riscos associados \u00e0 entrada", "text": "1. Riscos associados \u00e0 entrada", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 32.0, "t": 732.376, "r": 192.378, "b": 722.047, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "Fase de treinamento e ajuste", "text": "Fase de treinamento e ajuste", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 36.2, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 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riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/110", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 22, "bbox": {"l": 32.0, "t": 753.772, "r": 213.159, "b": 741.86, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "Exemplos de riscos: desafios", "text": "Exemplos de riscos: desafios", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/111", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 22, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 40.4, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "22", "text": "22", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/112", "parent": {"cref": "#/body"}, 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[{"page_no": 23, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 40.4, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "23", "text": "23", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/115", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 23, "bbox": {"l": 318.0, "t": 36.88599999999997, "r": 542.943, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/116", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 24, "bbox": {"l": 32.0, "t": 674.032, "r": 237.968, "b": 623.772, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Princ\u00edpios, pilares e controle", "text": "Princ\u00edpios, pilares e controle", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/117", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 24, "bbox": {"l": 32.0, "t": 417.584, "r": 273.128, "b": 410.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "Os Princ\u00edpios para Confian\u00e7a e Transpar\u00eancia da IBM e os Pilares", "text": "Os Princ\u00edpios para Confian\u00e7a e Transpar\u00eancia da IBM e os Pilares", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/118", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 24, "bbox": {"l": 32.0, "t": 405.584, "r": 290.638, "b": 266.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 789]}], "orig": "para IA confi\u00e1vel s\u00e3o a base para as iniciativas de \u00e9tica em IA da IBM. A\u00a0IBM estabeleceu um Conselho de \u00c9tica em IA com a miss\u00e3o de apoiar um processo centralizado de controle, revis\u00e3o e tomada de decis\u00f5es para pol\u00edticas, pr\u00e1ticas, comunica\u00e7\u00f5es, pesquisa, produtos e servi\u00e7os de \u00e9tica em IA da IBM. O conselho inclui um conjunto diversificado de stakeholders de toda a empresa e \u00e9 apoiado por uma comunidade de funcion\u00e1rios da IBM que atuam como pontos focais de IA e defensores da \u00e9tica em IA. Por meio do conselho, os princ\u00edpios da IBM s\u00e3o colocados em pr\u00e1tica. Conforme novas tecnologias surgem, como modelos de base, o Conselho de \u00c9tica em IA da IBM est\u00e1 ativamente engajado em apoiar o alinhamento com esses Princ\u00edpios e Pilares, que evoluem para abordar novas quest\u00f5es \u00e9ticas em IA.", "text": "para IA confi\u00e1vel s\u00e3o a base para as iniciativas de \u00e9tica em IA da IBM. A\u00a0IBM estabeleceu um Conselho de \u00c9tica em IA com a miss\u00e3o de apoiar um processo centralizado de controle, revis\u00e3o e tomada de decis\u00f5es para pol\u00edticas, pr\u00e1ticas, comunica\u00e7\u00f5es, pesquisa, produtos e servi\u00e7os de \u00e9tica em IA da IBM. O conselho inclui um conjunto diversificado de stakeholders de toda a empresa e \u00e9 apoiado por uma comunidade de funcion\u00e1rios da IBM que atuam como pontos focais de IA e defensores da \u00e9tica em IA. Por meio do conselho, os princ\u00edpios da IBM s\u00e3o colocados em pr\u00e1tica. Conforme novas tecnologias surgem, como modelos de base, o Conselho de \u00c9tica em IA da IBM est\u00e1 ativamente engajado em apoiar o alinhamento com esses Princ\u00edpios e Pilares, que evoluem para abordar novas quest\u00f5es \u00e9ticas em IA.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/119", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 24, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 40.4, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "24", "text": "24", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/120", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 24, "bbox": {"l": 318.0, "t": 36.88599999999997, "r": 542.943, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/121", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 25, "bbox": {"l": 32.0, "t": 674.032, "r": 170.072, "b": 623.772, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Prote\u00e7\u00f5es e mitiga\u00e7\u00f5es", "text": "Prote\u00e7\u00f5es e mitiga\u00e7\u00f5es", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/122", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 25, "bbox": {"l": 32.0, "t": 417.584, "r": 290.038, "b": 266.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 817]}], "orig": "A IBM estabeleceu uma cultura organizacional que apoia o desenvolvimento e o uso respons\u00e1veis de IA. Conforme indicado no relat\u00f3rio de \u00e9tica em a\u00e7\u00e3o na IA do IBM Institute for Business Value, a \u00e9tica em IA j\u00e1 se tornou mais orientada pelos neg\u00f3cios do que pela tecnologia, e os executivos n\u00e3o t\u00e9cnicos agora s\u00e3o os principais defensores da \u00e9tica em IA, aumentando de 15% em 2018 para 80% 3\u00a0anos depois. Al\u00e9m disso, 79% dos CEOs est\u00e3o agora preparados para agir em quest\u00f5es \u00e9ticas de IA, contra 20%. Reconhecemos que a IA respons\u00e1vel \u00e9 uma \u00e1rea sociot\u00e9cnica que necessita de um investimento hol\u00edstico em cultura, processos e ferramentas. Nosso investimento em cultura organizacional pr\u00f3pria inclui a montagem de equipes inclusivas e multidisciplinares e o estabelecimento de processos e estruturas para\u00a0avaliar riscos.", "text": "A IBM estabeleceu uma cultura organizacional que apoia o desenvolvimento e o uso respons\u00e1veis de IA. Conforme indicado no relat\u00f3rio de \u00e9tica em a\u00e7\u00e3o na IA do IBM Institute for Business Value, a \u00e9tica em IA j\u00e1 se tornou mais orientada pelos neg\u00f3cios do que pela tecnologia, e os executivos n\u00e3o t\u00e9cnicos agora s\u00e3o os principais defensores da \u00e9tica em IA, aumentando de 15% em 2018 para 80% 3\u00a0anos depois. Al\u00e9m disso, 79% dos CEOs est\u00e3o agora preparados para agir em quest\u00f5es \u00e9ticas de IA, contra 20%. Reconhecemos que a IA respons\u00e1vel \u00e9 uma \u00e1rea sociot\u00e9cnica que necessita de um investimento hol\u00edstico em cultura, processos e ferramentas. Nosso investimento em cultura organizacional pr\u00f3pria inclui a montagem de equipes inclusivas e multidisciplinares e o estabelecimento de processos e estruturas para\u00a0avaliar riscos.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/123", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 25, "bbox": {"l": 32.0, "t": 249.58400000000006, "r": 295.888, "b": 74.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 985]}], "orig": "A IBM est\u00e1 engajada em pesquisa de ponta e desenvolvimento de ferramentas para ajudar os profissionais de suporte durante todo o\u00a0ciclo de vida da IA respons\u00e1vel e confi\u00e1vel. A plataforma de IA e dados empresariais watsonx, \u00e9 desenvolvida com 3 componentes: o IBM watsonx.ai\u2122 AI studio, o armazenamento de dados IBM watsonx.data\u2122 e\u00a0o kit de ferramentas IBM watsonx.governance\u2122. A tecnologia de controle de IA da IBM permite que os usu\u00e1rios promovam fluxos de trabalho de IA respons\u00e1veis, transparentes e explic\u00e1veis. Essa tecnologia inclui o IBM Watson OpenScale, que monitora e mede os resultados dos modelos de IA ao longo de seu ciclo de vida e auxilia as organiza\u00e7\u00f5es na supervis\u00e3o de aspectos como justi\u00e7a, explicabilidade, resili\u00eancia, alinhamento com resultados de neg\u00f3cios e conformidade. A\u00a0IBM tamb\u00e9m desenvolveu v\u00e1rios m\u00e9todos para ajudar com problemas de vi\u00e9s como FairIJ, Equi-tuning e FairReprogram. Leia mais sobre outras ferramentas de IA de software livre e confi\u00e1veis .", "text": "A IBM est\u00e1 engajada em pesquisa de ponta e desenvolvimento de ferramentas para ajudar os profissionais de suporte durante todo o\u00a0ciclo de vida da IA respons\u00e1vel e confi\u00e1vel. A plataforma de IA e dados empresariais watsonx, \u00e9 desenvolvida com 3 componentes: o IBM watsonx.ai\u2122 AI studio, o armazenamento de dados IBM watsonx.data\u2122 e\u00a0o kit de ferramentas IBM watsonx.governance\u2122. A tecnologia de controle de IA da IBM permite que os usu\u00e1rios promovam fluxos de trabalho de IA respons\u00e1veis, transparentes e explic\u00e1veis. Essa tecnologia inclui o IBM Watson OpenScale, que monitora e mede os resultados dos modelos de IA ao longo de seu ciclo de vida e auxilia as organiza\u00e7\u00f5es na supervis\u00e3o de aspectos como justi\u00e7a, explicabilidade, resili\u00eancia, alinhamento com resultados de neg\u00f3cios e conformidade. A\u00a0IBM tamb\u00e9m desenvolveu v\u00e1rios m\u00e9todos para ajudar com problemas de vi\u00e9s como FairIJ, Equi-tuning e FairReprogram. Leia mais sobre outras ferramentas de IA de software livre e confi\u00e1veis .", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 25, "bbox": {"l": 318.0, "t": 417.584, "r": 482.919, "b": 410.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "As prote\u00e7\u00f5es e mitiga\u00e7\u00f5es adicionais incluem:", "text": "As prote\u00e7\u00f5es e mitiga\u00e7\u00f5es adicionais incluem:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 25, "bbox": {"l": 318.0, "t": 393.584, "r": 420.342, "b": 386.698, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "Relat\u00f3rios de transpar\u00eancia", "text": "Relat\u00f3rios de transpar\u00eancia", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 25, "bbox": {"l": 318.0, "t": 381.584, "r": 566.782, "b": 350.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 156]}], "orig": "Usar modelos de fichas t\u00e9cnicas padronizadas \u00e9 uma maneira de registrar com precis\u00e3o detalhes sobre os dados e modelos, prop\u00f3sito e\u00a0poss\u00edveis usos e riscos.", "text": "Usar modelos de fichas t\u00e9cnicas padronizadas \u00e9 uma maneira de registrar com precis\u00e3o detalhes sobre os dados e modelos, prop\u00f3sito e\u00a0poss\u00edveis usos e riscos.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/127", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 25, "bbox": {"l": 318.0, "t": 345.584, "r": 377.424, "b": 338.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Leia mais aqui \u2192", "text": "Leia mais aqui \u2192", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 25, "bbox": {"l": 318.0, "t": 321.584, "r": 437.062, "b": 314.698, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Filtragem de dados indesej\u00e1veis", "text": "Filtragem de dados indesej\u00e1veis", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 25, "bbox": {"l": 318.0, "t": 309.584, "r": 568.23, "b": 254.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 298]}], "orig": "Usar dados de qualidade superior e selecionados pode ajudar a mitigar determinados problemas. A IBM est\u00e1 desenvolvendo t\u00e9cnicas de filtragem para ajudar a reduzir as chances de produzir conte\u00fado indesej\u00e1vel e desalinhado por remover linguagem de \u00f3dio, linguagem tendenciosa e profanidade dos dados.", "text": "Usar dados de qualidade superior e selecionados pode ajudar a mitigar determinados problemas. A IBM est\u00e1 desenvolvendo t\u00e9cnicas de filtragem para ajudar a reduzir as chances de produzir conte\u00fado indesej\u00e1vel e desalinhado por remover linguagem de \u00f3dio, linguagem tendenciosa e profanidade dos dados.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/130", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 25, "bbox": {"l": 318.0, "t": 249.58400000000006, "r": 377.424, "b": 242.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Leia mais aqui \u2192", "text": "Leia mais aqui \u2192", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 25, "bbox": {"l": 318.0, "t": 225.58400000000006, "r": 401.016, "b": 218.69799999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Adapta\u00e7\u00e3o de dom\u00ednio", "text": "Adapta\u00e7\u00e3o de dom\u00ednio", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 25, "bbox": {"l": 318.0, "t": 213.58400000000006, "r": 572.174, "b": 158.77700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 284]}], "orig": "Treinar um modelo de base para um dom\u00ednio ou setor espec\u00edfico pode ajudar a minimizar o escopo de risco para o qual os modelos podem dar\u00a0origem, pois ele pode ser condicionado a gerar resultados que s\u00e3o ajustados para serem mais relevantes para esse dom\u00ednio ou setor. Leia mais aqui \u2192", "text": "Treinar um modelo de base para um dom\u00ednio ou setor espec\u00edfico pode ajudar a minimizar o escopo de risco para o qual os modelos podem dar\u00a0origem, pois ele pode ser condicionado a gerar resultados que s\u00e3o ajustados para serem mais relevantes para esse dom\u00ednio ou setor. Leia mais aqui \u2192", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 25, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 40.4, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "25", "text": "25", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 25, "bbox": {"l": 318.0, "t": 36.88599999999997, "r": 542.943, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/135", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 26, "bbox": {"l": 32.0, "t": 753.584, "r": 201.576, "b": 746.698, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "Supervis\u00e3o humana e an\u00e1lise humana no loop", "text": "Supervis\u00e3o humana e an\u00e1lise humana no loop", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 26, "bbox": {"l": 32.0, "t": 741.584, "r": 289.301, "b": 686.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 313]}], "orig": "A supervis\u00e3o e revis\u00e3o humanas podem ajudar a identificar e corrigir erros e vieses no output gerado. Al\u00e9m disso, a valida\u00e7\u00e3o e o feedback humanos sobre a qualidade das respostas do modelo ajudam a garantir que o conte\u00fado gerado seja preciso, relevante, de alta qualidade, n\u00e3o\u00a0esteja divergindo e esteja alinhado.", "text": "A supervis\u00e3o e revis\u00e3o humanas podem ajudar a identificar e corrigir erros e vieses no output gerado. Al\u00e9m disso, a valida\u00e7\u00e3o e o feedback humanos sobre a qualidade das respostas do modelo ajudam a garantir que o conte\u00fado gerado seja preciso, relevante, de alta qualidade, n\u00e3o\u00a0esteja divergindo e esteja alinhado.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 26, "bbox": {"l": 32.0, "t": 681.584, "r": 91.424, "b": 674.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Leia mais aqui \u2192", "text": "Leia mais aqui \u2192", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 26, "bbox": {"l": 32.0, "t": 657.584, "r": 137.503, "b": 650.698, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "Compromisso de consultoria", "text": "Compromisso de consultoria", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 26, "bbox": {"l": 32.0, "t": 645.584, "r": 294.077, "b": 578.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 402]}], "orig": "A IBM Consulting se dedica a ajudar os clientes com o uso seguro e respons\u00e1vel da IA, independentemente do stack tecnol\u00f3gico preferido. 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Essa diversidade melhora a precis\u00e3o e o desempenho dos sistemas de IA e ajuda a reduzir os riscos ao longo do ciclo de vida de IA, incluindo o potencial para desfechos adversos que afetam grupos que podem n\u00e3o ser bem representados em equipes menos diversificadas.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/150", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 26, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 40.4, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "26", "text": "26", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/151", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 26, "bbox": {"l": 318.0, "t": 36.88599999999997, "r": 542.943, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/152", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 27, "bbox": {"l": 32.0, "t": 674.032, "r": 293.528, "b": 593.772, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "Pol\u00edticas, regulamentos e melhores pr\u00e1ticas de\u00a0IA", "text": "Pol\u00edticas, regulamentos e melhores pr\u00e1ticas de\u00a0IA", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/153", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 27, "bbox": {"l": 32.0, "t": 417.584, "r": 295.045, "b": 302.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "Um Guia dos Formuladores de Pol\u00edticas para Modelos de Base apresenta o que os formuladores de pol\u00edticas precisam saber sobre modelos de base. 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Este blog, do Laborat\u00f3rio de Pol\u00edticas da IBM, tem como objetivo ajudar os formuladores de pol\u00edticas na tarefa complexa de regular o\u00a0uso de IA generativa, visando evitar os riscos sem limitar a inova\u00e7\u00e3o e as oportunidades ben\u00e9ficas. 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"label": "text", "prov": [{"page_no": 27, "bbox": {"l": 318.0, "t": 417.584, "r": 579.061, "b": 302.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 620]}], "orig": "A IBM tem parcerias acad\u00eamicas s\u00f3lidas, como o MIT-IBM Watson AI\u00a0Lab, onde uma comunidade de cientistas do MIT e da IBM Research conduzem pesquisas sobre IA e trabalham com organiza\u00e7\u00f5es globais para unir algoritmos ao seu impacto nos neg\u00f3cios e na sociedade. O\u00a0Notre Dame-IBM Tech Ethics Lab foi formado para abordar as diversas quest\u00f5es \u00e9ticas implicadas pelo desenvolvimento e uso de tecnologias avan\u00e7adas, incluindo IA, aprendizado de m\u00e1quina (ML) e computa\u00e7\u00e3o qu\u00e2ntica. A pesquisa de Intelig\u00eancia Artificial Centrada no Homem (HAI) da Universidade de Stanford promove pesquisas, educa\u00e7\u00e3o, pol\u00edticas e\u00a0pr\u00e1ticas de IA.", "text": "A IBM tem parcerias acad\u00eamicas s\u00f3lidas, como o MIT-IBM Watson AI\u00a0Lab, onde uma comunidade de cientistas do MIT e da IBM Research conduzem pesquisas sobre IA e trabalham com organiza\u00e7\u00f5es globais para unir algoritmos ao seu impacto nos neg\u00f3cios e na sociedade. O\u00a0Notre Dame-IBM Tech Ethics Lab foi formado para abordar as diversas quest\u00f5es \u00e9ticas implicadas pelo desenvolvimento e uso de tecnologias avan\u00e7adas, incluindo IA, aprendizado de m\u00e1quina (ML) e computa\u00e7\u00e3o qu\u00e2ntica. A pesquisa de Intelig\u00eancia Artificial Centrada no Homem (HAI) da Universidade de Stanford promove pesquisas, educa\u00e7\u00e3o, pol\u00edticas e\u00a0pr\u00e1ticas de IA.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/165", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 27, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 40.4, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/166", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 27, "bbox": {"l": 318.0, "t": 36.88599999999997, "r": 542.943, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/167", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 28, "bbox": {"l": 32.0, "t": 753.772, "r": 297.045, "b": 641.83, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "Continue acompanhando este espa\u00e7o para obter mais informa\u00e7\u00f5es sobre os \u00faltimos avan\u00e7os em modelos de base e como a IBM est\u00e1 trabalhando para o desenvolvimento respons\u00e1vel e uso desta e de outras tecnologias.", "text": "Continue acompanhando este espa\u00e7o para obter mais informa\u00e7\u00f5es sobre os \u00faltimos avan\u00e7os em modelos de base e como a IBM est\u00e1 trabalhando para o desenvolvimento respons\u00e1vel e uso desta e de outras tecnologias.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/168", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 28, "bbox": {"l": 32.0, "t": 36.88599999999997, "r": 40.4, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/169", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 28, "bbox": {"l": 318.0, "t": 36.88599999999997, "r": 542.943, "b": 30.92999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "text": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/170", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 29, "bbox": {"l": 32.0, "t": 488.886, "r": 161.681, "b": 482.93, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "\u00a9 Copyright IBM Corporation 2023, 2024", "text": "\u00a9 Copyright IBM Corporation 2023, 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/171", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 29, "bbox": {"l": 32.0, "t": 470.889, "r": 93.712, "b": 410.921, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "IBM Brasil Ltda Rua Tut\u00f3ia, 1157 CEP 04007-900 S\u00e3o Paulo, SP IBM Corporation New Orchard Road Armonk, NY 10504", "text": "IBM Brasil Ltda Rua Tut\u00f3ia, 1157 CEP 04007-900 S\u00e3o Paulo, SP IBM Corporation New Orchard Road Armonk, NY 10504", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/172", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 29, "bbox": {"l": 32.0, "t": 398.873, "r": 119.311, "b": 374.913, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Produzido nos Estados Unidos da Am\u00e9rica Fevereiro de 2024", "text": "Produzido nos Estados Unidos da Am\u00e9rica Fevereiro de 2024", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/173", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 29, "bbox": {"l": 32.0, "t": 362.872, "r": 295.018, "b": 311.906, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 464]}], "orig": "IBM, o logotipo da IBM, Enterprise Design Thinking, IBM Consulting, IBM Research, IBM Watson, watsonx, watsonx.ai, watsonx.data e watsonx.governance s\u00e3o marcas comerciais ou marcas registradas da International Business Machines Corporation, nos Estados Unidos e/ou em outros pa\u00edses. Outros nomes de produtos e servi\u00e7os podem ser marcas comerciais da IBM ou de outras empresas. Uma lista atual de marcas comerciais da IBM est\u00e1 dispon\u00edvel em ibm.com/br-pt/trademark.", "text": "IBM, o logotipo da IBM, Enterprise Design Thinking, IBM Consulting, IBM Research, IBM Watson, watsonx, watsonx.ai, watsonx.data e watsonx.governance s\u00e3o marcas comerciais ou marcas registradas da International Business Machines Corporation, nos Estados Unidos e/ou em outros pa\u00edses. Outros nomes de produtos e servi\u00e7os podem ser marcas comerciais da IBM ou de outras empresas. Uma lista atual de marcas comerciais da IBM est\u00e1 dispon\u00edvel em ibm.com/br-pt/trademark.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/174", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 29, "bbox": {"l": 32.0, "t": 299.865, "r": 295.085, "b": 275.905, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 188]}], "orig": "Este documento \u00e9 atual na data de sua publica\u00e7\u00e3o inicial, podendo ser alterado pela IBM a qualquer momento. Nem todas as ofertas est\u00e3o dispon\u00edveis em todos os pa\u00edses nos quais a IBM opera.", "text": "Este documento \u00e9 atual na data de sua publica\u00e7\u00e3o inicial, podendo ser alterado pela IBM a qualquer momento. Nem todas as ofertas est\u00e3o dispon\u00edveis em todos os pa\u00edses nos quais a IBM opera.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/175", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 29, "bbox": {"l": 32.0, "t": 263.86400000000003, "r": 295.26, "b": 212.89800000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 367]}], "orig": "AS INFORMA\u00c7\u00d5ES CONTIDAS NESTE DOCUMENTO S\u00c3O FORNECIDAS NO ESTADO EM QUE SEM ENCONTRAM, SEM QUALQUER GARANTIA, EXPRESSA OU IMPL\u00cdCITA, INCLUSIVE SEM QUALQUER GARANTIA DE COMERCIALIZA\u00c7\u00c3O, ADEQUA\u00c7\u00c3O A DETERMINADO FIM E QUALQUER GARANTIA OU CONDI\u00c7\u00c3O DE N\u00c3O INFRA\u00c7\u00c3O. Os produtos IBM t\u00eam a garantia prevista nos termos e condi\u00e7\u00f5es dos contratos sob os quais s\u00e3o fornecidos.", "text": "AS INFORMA\u00c7\u00d5ES CONTIDAS NESTE DOCUMENTO S\u00c3O FORNECIDAS NO ESTADO EM QUE SEM ENCONTRAM, SEM QUALQUER GARANTIA, EXPRESSA OU IMPL\u00cdCITA, INCLUSIVE SEM QUALQUER GARANTIA DE COMERCIALIZA\u00c7\u00c3O, ADEQUA\u00c7\u00c3O A DETERMINADO FIM E QUALQUER GARANTIA OU CONDI\u00c7\u00c3O DE N\u00c3O INFRA\u00c7\u00c3O. Os produtos IBM t\u00eam a garantia prevista nos termos e condi\u00e7\u00f5es dos contratos sob os quais s\u00e3o fornecidos.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/176", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 29, "bbox": {"l": 32.0, "t": 200.85699999999997, "r": 292.264, "b": 149.89099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "Declara\u00e7\u00e3o de boas pr\u00e1ticas de seguran\u00e7a: nenhum sistema ou produto de TI deve ser considerado completamente seguro, e nenhuma medida exclusiva de produto, servi\u00e7o ou seguran\u00e7a pode ser completamente eficaz na preven\u00e7\u00e3o de uso ou acesso inadequado. A IBM n\u00e3o garante que nenhum de seus sistemas, produtos ou servi\u00e7os estejam imunes nem que tornar\u00e3o sua empresa imune a condutas maliciosas ou ilegais por parte de terceiros.", "text": "Declara\u00e7\u00e3o de boas pr\u00e1ticas de seguran\u00e7a: nenhum sistema ou produto de TI deve ser considerado completamente seguro, e nenhuma medida exclusiva de produto, servi\u00e7o ou seguran\u00e7a pode ser completamente eficaz na preven\u00e7\u00e3o de uso ou acesso inadequado. A IBM n\u00e3o garante que nenhum de seus sistemas, produtos ou servi\u00e7os estejam imunes nem que tornar\u00e3o sua empresa imune a condutas maliciosas ou ilegais por parte de terceiros.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/177", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 29, "bbox": {"l": 32.0, "t": 137.85000000000002, "r": 295.043, "b": 86.88400000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 453]}], "orig": "O cliente \u00e9 respons\u00e1vel por garantir o cumprimento de todas as leis e regulamentos aplic\u00e1veis. A IBM n\u00e3o fornece conselho jur\u00eddico tampouco representa ou garante que seus servi\u00e7os ou produtos garantir\u00e3o que o cliente esteja em conformidade com qualquer lei ou regulamenta\u00e7\u00e3o. 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Kush Varshney, \nManish Bhide, Manish Goyal, Melis Kiziltay, Michael Epstein, Michael \nHind, Milena Pribic, Phaedra Boinodiris, Rogerio Abreu de Paula, \nSaishruthi Swaminathan e Suj Perepa.\n\n\n\n3\n\n\u00cdndice\n\n04\n \nExecutivo \nResumo\n\n05\n \nIntrodu\u00e7\u00e3o\n\n06\n \nBenef\u00edcios dos  \nmodelos de base\n\n08\n \nRiscos dos  \nmodelos de base\n\n16\n \nRisco \nExemplos\n\n24\n \nPrinc\u00edpios, pilares \ne governan\u00e7a\n\n25\n \nProte\u00e7\u00f5es \ne mitiga\u00e7\u00f5es\n\n27\n \nPol\u00edticas, regulamenta\u00e7\u00f5es \ne\u00a0melhores pr\u00e1ticas de IA \nExemplos\n\n\n\n4 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nResumo executivo\n\nA ascens\u00e3o dos modelos de base oferece \u00e0s empresas novas e \nempolgantes possibilidades, mas tamb\u00e9m levanta quest\u00f5es novas e \namplas sobre design, desenvolvimento, implementa\u00e7\u00e3o e uso \u00e9tico. \nSegundo uma recente pesquisa sobre IA generativa do IBM Institute for \nBusiness Value, as organiza\u00e7\u00f5es j\u00e1 est\u00e3o manifestando preocupa\u00e7\u00f5es \nsobre quest\u00f5es relacionadas \u00e0 confian\u00e7a, especificamente como \nbarreiras para investimentos. Suas principais preocupa\u00e7\u00f5es s\u00e3o \nciberseguran\u00e7a (57%), privacidade (51%) e precis\u00e3o (47%). Muitas \norganiza\u00e7\u00f5es estavam levando essas preocupa\u00e7\u00f5es a s\u00e9rio antes \nda \u2018consumeriza\u00e7\u00e3o\u2019 da IA generativa, expressando sua inten\u00e7\u00e3o de \ninvestir pelo menos 40% mais em \u00e9tica de IA nos pr\u00f3ximos tr\u00eas anos. \nA\u00a0conscientiza\u00e7\u00e3o sobre riscos e poss\u00edveis maneiras de mitig\u00e1-los \u00e9 o  \nprimeiro passo crucial para a cria\u00e7\u00e3o de sistemas de IA confi\u00e1veis.\n\nNeste documento:\n\nExploraremos as vantagens dos modelos de base, incluindo \nsua capacidade de realizar tarefas desafiadoras, potencial \npara acelerar a ado\u00e7\u00e3o de IA, habilidade de aumentar \na produtividade e os benef\u00edcios econ\u00f4micos que eles \nproporcionam. \n\nDiscutiremos as tr\u00eas categorias de risco, incluindo riscos \nconhecidos de formas anteriores de IA, riscos conhecidos \namplificados por modelos de base e riscos emergentes \nintr\u00ednsecos aos recursos generativos dos modelos de base. \n\nAbordaremos os princ\u00edpios, os pilares e o controle que \nformam a base das iniciativas \u00e9ticas de IA da IBM e \nsugeriremos barreiras para a mitiga\u00e7\u00e3o de riscos.\n\nhttps://www.ibm.com/thought-leadership/institute-business-value/report/enterprise-generative-ai\nhttps://www.ibm.com/thought-leadership/institute-business-value/br-pt/report/enterprise-generative-ai\n\n\n5 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nIntrodu\u00e7\u00e3o\n\n\u00c0 medida que o uso de IA continua se expandindo, os grandes e \ncomplexos modelos de IA est\u00e3o fornecendo resultados promissores \nde desempenho, bem como resolvendo alguns dos problemas mais \ndesafiadores da sociedade. No entanto, criar grandes conjuntos de dados \nde treinamento e modelos complexos para cada aplicativo de IA pode \nser extremamente dif\u00edcil para as empresas. Modelos de base fornecem \num caminho para alcan\u00e7ar o melhor dos dois mundos: desenvolver \nmodelos de \u00faltima gera\u00e7\u00e3o poderosos e reutiliz\u00e1-los diretamente ou \naplicar m\u00e9todos de ajuste para implementar uma variedade de casos \nde uso, em vez de treinar novos modelos para cada caso de uso. Por \nexemplo, a IBM Research desenvolveu modelos de base para inspe\u00e7\u00e3o \nvisual. Esses modelos de base aprendem a representa\u00e7\u00e3o geral de \nsuperf\u00edcies e corredores de concreto e podem ser ajustados ainda \nmais para casos de uso espec\u00edficos, como detec\u00e7\u00e3o de rachaduras ou \ninspe\u00e7\u00e3o de defeitos com dados menos rotulados.\n\nA IBM define um modelo de base como um modelo de IA que pode \nser adaptado a uma ampla gama de tarefas de recebimento de dados. \nOs\u00a0modelos de base normalmente s\u00e3o modelos generativos de grande \nescala treinados em dados n\u00e3o rotulados usando autossupervis\u00e3o. \nComo\u00a0modelos de grande escala, os modelos de base podem incluir \nbilh\u00f5es de par\u00e2metros.\n\nA IBM \u00e9 uma empresa de nuvem h\u00edbrida e IA com vasta reputa\u00e7\u00e3o como \nadministradora de dados respons\u00e1vel e comprometida com a \u00e9tica em \nIA. Usando a capacidade de nossas equipes de pesquisa, produto e \nconsultoria, juntamente com parceiros externos, como a Hugging Face, \najudamos a trazer o poder dos modelos de base para nossos clientes \ne a criar IAs confi\u00e1veis em qualquer empresa. A IBM tamb\u00e9m continua \ninvestindo na cria\u00e7\u00e3o de novas plataformas, como a IA IBM watsonx \ne plataformas e tecnologias de dados, para projetar e desenvolver \nmodelos de IA para se comportar de maneira audit\u00e1vel e confi\u00e1vel. \n\nEste documento descreve o ponto de vista da IBM sobre a \u00e9tica dos \nmodelos de base. \u00c9 a primeira vers\u00e3o, e as vers\u00f5es futuras expandir\u00e3o \nv\u00e1rios aspectos da abordagem \u00e9tica do modelo de base da IBM. \nEsperamos que este documento seja \u00fatil para todos os stakeholders no \ndesenvolvimento, implementa\u00e7\u00e3o e uso do modelo de base de forma \nrespons\u00e1vel.\n\nhttps://research.ibm.com/blog/ai-inspection-runways\nhttps://research.ibm.com/blog/ai-inspection-runways\nhttps://www.ibm.com/br-pt/artificial-intelligence/ethics\nhttps://www.ibm.com/br-pt/artificial-intelligence/ethics\nhttps://research.ibm.com/topics/foundation-models\nhttps://www.ibm.com/br-pt/watsonx\nhttps://www.ibm.com/br-pt/consulting/artificial-intelligence\nhttps://newsroom.ibm.com/2023-05-09-IBM-Unveils-the-Watsonx-Platform-to-Power-Next-Generation-Foundation-Models-for-Business\nhttps://www.ibm.com/br-pt/watson\n\n\n6 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nBenef\u00edcios dos  \nmodelos de base\n\nOs modelos de base podem melhorar significativamente o processo de \ndesenvolvimento de sistemas de IA e auxiliar no avan\u00e7o da IA da fase de \nexplora\u00e7\u00e3o para a ado\u00e7\u00e3o nas empresas. Seus benef\u00edcios incluem:\n\nRealizar tarefas complexas \nModelos de base mostram um aumento significativo no desempenho \nna resolu\u00e7\u00e3o de problemas complexos e dif\u00edceis. Por exemplo, \no modelo de base geoespacial da colabora\u00e7\u00e3o IBM e NASA foi \nprojetado para converter os dados de sat\u00e9lite da NASA em mapas \nde desastres naturais, como inunda\u00e7\u00f5es e outras mudan\u00e7as de \ncen\u00e1rio. O modelo tamb\u00e9m pode ser usado para ajudar a revelar \no\u00a0passado do nosso planeta; estimar riscos para culturas, empresas \nou infraestruturas devido ao clima severo; desenvolver estrat\u00e9gias \npara se adaptar \u00e0s mudan\u00e7as clim\u00e1ticas; e auxiliar no agroneg\u00f3cio. \nO modelo est\u00e1 planejado para ser disponibilizado previamente aos \nclientes IBM por meio do IBM Environmental Intelligence Suite.\n\nPara ilustrar, o MoLFormer-XL da IBM \u00e9 um modelo de base que \n\u00e9\u00a0capaz de inferir a estrutura de mol\u00e9culas a partir de representa\u00e7\u00f5es \nsimples, tornando mais f\u00e1cil a aprendizagem de v\u00e1rias tarefas \nde recebimento de dados, como prever as propriedades f\u00edsicas \ne\u00a0qu\u00e2nticas de uma mol\u00e9cula, identificar mol\u00e9culas semelhantes, \nrastrear mol\u00e9culas j\u00e1 aprovadas para novos casos de uso e descobrir \nnovas mol\u00e9culas. Moderna e IBM est\u00e3o explorando formas de \nusar o MoLExer para ajudar a prever propriedades das mol\u00e9culas e \nentender as caracter\u00edsticas de poss\u00edveis medicamentos de mRNA.\n\nMaior produtividade \nA natureza generativa dos modelos de base amplia o n\u00famero de \n\u00e1reas em que a IA pode ser usada em uma empresa para ajudar a \nmelhorar a\u00a0produtividade, automatizando tarefas rotineiras e tediosas \ne\u00a0permitindo que os usu\u00e1rios dediquem mais tempo ao trabalho \ncriativo e inovador. Por exemplo, o IBM Watsonx Code Assistant, \ndesenvolvido com modelos de base, possibilita que desenvolvedores, \nindependentemente do n\u00edvel de experi\u00eancia, escrevam c\u00f3digos usando \nrecomenda\u00e7\u00f5es geradas por IA.\n\nTime to value mais r\u00e1pido \nModelos de base geralmente s\u00e3o treinados com dados n\u00e3o rotulados, \nque est\u00e3o mais dispon\u00edveis em grandes quantidades do que dados \nrotulados. Uma vez treinados, os modelos de base podem ser usados \ndiretamente ou ap\u00f3s serem ajustados para aplicativos de recebimento \nde dados, usando uma pequena quantidade de dados rotulados  \nespecializados, que podem diminuir a cria\u00e7\u00e3o do time to value.\n\nhttps://research.ibm.com/blog/geospatial-models-nasa-ai\nhttps://research.ibm.com/blog/ibm-nasa-foundation-models\nhttps://www.ibm.com/br-pt/products/environmental-intelligence-suite\nhttps://research.ibm.com/blog/molecular-transformer-discovery\nhttps://newsroom.ibm.com/2023-04-20-Moderna-and-IBM-to-Explore-Quantum-Computing-and-Generative-AI-for-mRNA-Science\nhttps://www.ibm.com/br-pt/products/watsonx-code-assistant\nhttps://research.ibm.com/blog/ai-for-code-project-wisdom-red-hat\n\n\n7 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nUtilize diversas modalidades de dados \nOs modelos de base podem ser treinados usando diversas modalidades \nde dados, como l\u00edngua natural, texto, imagem e \u00e1udio. Eles tamb\u00e9m \npodem ser aplicados a tarefas que exigem diferentes tipos de dados, \ncomo dados de s\u00e9ries temporais, dados geoespaciais, dados tabulares, \ndados semiestruturados e dados de modalidade mista, como texto \ncombinado com imagens.\n\nDespesas amortizadas \nEmbora o custo inicial do treinamento de um modelo de base seja \nsignificativamente maior do que o treinamento de um modelo de IA \ntradicional, o custo adicional para aplic\u00e1-lo em uma nova tarefa \u00e9 \nconsideravelmente menor. O uso de modelos de base pr\u00e9-treinados  \npoderia ajudar a eliminar a necessidade de que as empresas fa\u00e7am \ninvestimentos substanciais para treinar modelos de base e explorar suas \nnovas capacidades. Para uma empresa, a confiabilidade dos modelos, \na\u00a0efici\u00eancia energ\u00e9tica, o desempenho, a portabilidade e a capacidade \nde\u00a0usar dados corporativos de forma eficaz e segura s\u00e3o fundamentais.\n\nA IBM permite que as empresas \ncriem e detenham o valor de \nmodelos de base para seus \nneg\u00f3cios, trazendo as melhores \ninova\u00e7\u00f5es da comunidade de \nIA aberta e global, operando de \nforma eficiente em ambientes de \ncomputa\u00e7\u00e3o h\u00edbrida, ajudando \na mitigar riscos e controlando \nrigorosamente a IA.\n\n\n\n8 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nRiscos dos  \nmodelos de base\n\nComo todas as tecnologias que avan\u00e7am rapidamente, os modelos \nde base oferecem riscos e benef\u00edcios. Alguns s\u00e3o riscos legais, \ncomo restri\u00e7\u00f5es \u00e0 movimenta\u00e7\u00e3o ou uso de dados, e precisam \nser cuidadosamente avaliados de acordo com a legisla\u00e7\u00e3o atual e \nem evolu\u00e7\u00e3o. Outros riscos t\u00eam uma natureza \u00e9tica e devem ser \nconsiderados cuidadosamente para que a tecnologia tenha um impacto \npositivo. Em geral, os riscos de IA levantam quest\u00f5es sociot\u00e9cnicas \ne\u00a0devem ser abordados e mitigados por meio de m\u00e9todos sociot\u00e9cnicos, \nincluindo ferramentas de software, processos de avalia\u00e7\u00e3o de risco, \nframeworks de \u00e9tica em IA, mecanismos de controle, consultas \nmultistakeholder, padr\u00f5es e regulamenta\u00e7\u00e3o. Iremos listar os riscos \nconsiderando as seguintes 3 categorias:\n\n1. Tradicional. Riscos conhecidos de formas anteriores ou anteriores \nde\u00a0sistemas de IA\n\n2. Amplificados. Riscos conhecidos, mas agora intensificados devido \u00e0s \ncaracter\u00edsticas intr\u00ednsecas dos modelos de base, principalmente seus \nrecursos generativos inerentes\n\n3. Novo. Riscos emergentes intr\u00ednsecos aos modelos de base e suas \ncapacidades generativas inerentes\n\n \nTamb\u00e9m estruturamos a lista de riscos em rela\u00e7\u00e3o a se est\u00e3o \nprincipalmente associados ao conte\u00fado fornecido ao modelo \nbase, o\u00a0input, ou ao conte\u00fado gerado por ele, o output, ou se est\u00e3o \nrelacionados a desafios adicionais.\n\n\n\n9 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n1. Riscos associados \u00e0 entrada\n\nGrupo Risco Indicador\n\nFase de treinamento e ajuste\n\nJusti\u00e7a Vi\u00e9s de dados: vi\u00e9s hist\u00f3rico, representacional \ne social presente nos dados usados para \ntreinar e fazer o ajuste fino do modelo.\n\nAmplificadoTreinar um sistema de IA com dados enviesados, como vi\u00e9s hist\u00f3rico ou \nrepresentacional, pode resultar em outputs enviesados ou distorcidos \nque podem representar injustamente ou discriminar certos grupos \nou indiv\u00edduos. Al\u00e9m dos impactos negativos na sociedade, entidades \ncomerciais podem enfrentar consequ\u00eancias legais, interrup\u00e7\u00e3o \ndas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o decorrentes dos resultados \nenviesados do modelo.\n\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\n\nEnvenenamento de dados: um tipo de ataque \nadversarial no qual um advers\u00e1rio ou agente \ninterno malicioso injeta intencionalmente \namostras corrompidas, falsas, enganosas \nou incorretas no conjunto de dados de \ntreinamento ou ajuste fino.\n\nTradicionalO envenenamento de dados pode tornar o modelo sens\u00edvel a um padr\u00e3o \nde dados malicioso e produzir o output desejado pelo advers\u00e1rio. Isso \npode criar um risco de seguran\u00e7a onde advers\u00e1rios podem manipular \no\u00a0comportamento do modelo em seu pr\u00f3prio benef\u00edcio.  Al\u00e9m de produzir \nresultados n\u00e3o intencionais e potencialmente maliciosos, uma diverg\u00eancia \ndo modelo causada por envenenamento de dados pode resultar em \nentidades comerciais enfrentando consequ\u00eancias legais, interrup\u00e7\u00e3o \ndas\u00a0opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\n\nRobustez\n\nCuradoria de dados: quando os dados de \ntreinamento ou ajuste s\u00e3o coletados ou \npreparados de forma inadequada.\n\nAmplificadoUma curadoria de dados inadequada pode afetar adversamente como \num modelo \u00e9 treinado, resultando em um modelo que n\u00e3o se comporta \nde acordo com os valores pretendidos. Exemplos de uma curadoria de \ndados inadequada podem incluir erros de rotulagem ou anota\u00e7\u00e3o nos \ndados usados para treinar ou ajustar o modelo. Corrigir problemas ap\u00f3s \no treinamento e a implementa\u00e7\u00e3o do modelo pode ser insuficiente para \ngarantir um comportamento adequado. Um comportamento inadequado do \nmodelo pode resultar em entidades comerciais enfrentando consequ\u00eancias \nlegais, interrup\u00e7\u00f5es nas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\n\nAlinhamento \nde valor\n\nRetreinamento baseado em downstream: \nusando de outputs indesej\u00e1veis (imprecisos, \ninadequados, conte\u00fado do usu\u00e1rio, etc.) \nde aplica\u00e7\u00f5es downstream para fins de \nretreinamento.\n\nNovoO reaproveitamento de output downstream para treinar novamente um \nmodelo sem implementar a verifica\u00e7\u00e3o humana adequada aumenta as \nchances de que outputs indesej\u00e1veis sejam incorporados aos dados de \ntreinamento ou ajuste do modelo, possivelmente gerando outputs ainda \nmais indesej\u00e1veis.  Comportamento inadequado do modelo pode resultar \nem entidades empresariais enfrentando consequ\u00eancias legais ou danos \n\u00e0 reputa\u00e7\u00e3o.  N\u00e3o cumprir com as leis de transfer\u00eancia de dados pode \nresultar em multas e outras consequ\u00eancias legais.\n\nTransfer\u00eancia de dados: leis e outras \nrestri\u00e7\u00f5es podem limitar ou proibir a \ntransfer\u00eancia de dados.\n\nTradicionalRestri\u00e7\u00f5es \u00e0 transfer\u00eancia de dados podem afetar a disponibilidade dos \ndados necess\u00e1rios para treinar um modelo de IA e podem resultar em \ndados mal representados. Al\u00e9m do impacto na disponibilidade de dados, \no n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de transfer\u00eancia de dados \npode resultar em multas e outras consequ\u00eancias legais. \n\nLeis de dados\n\nUso de dados: leis e outras restri\u00e7\u00f5es podem \nlimitar ou proibir o uso de alguns dados para \ncasos de uso espec\u00edficos de IA.\n\nTradicionalO n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de uso de dados pode \nresultar em multas e outras consequ\u00eancias legais. \n\nAquisi\u00e7\u00e3o de dados: leis e outras \nregulamenta\u00e7\u00f5es podem limitar a coleta \nde certos tipos de dados para casos de uso \nespec\u00edficos de IA.\n\nAmplificadoO n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es da aquisi\u00e7\u00e3o de dados \npode resultar em multas e outras consequ\u00eancias legais. \n\n\n\n10 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nGrupo Risco Indicador\n\nPropriedade \nintelectual\n\nDireitos de uso de dados: termos de servi\u00e7o, \nleis de direitos autorais, conformidade com \nlicen\u00e7as ou outras quest\u00f5es de propriedade \nintelectual podem restringir a capacidade \nde usar certos dados para a constru\u00e7\u00e3o \nde\u00a0modelos. \n\nAmplificadoAs leis e regulamenta\u00e7\u00f5es referentes ao uso de dados para treinar IA \ns\u00e3o inst\u00e1veis e podem variar de pa\u00eds para pa\u00eds, o que cria desafios no \ndesenvolvimento de modelos. Se o uso de dados violar regras ou restri\u00e7\u00f5es, \nas entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\n\nTranspar\u00eancia de dados: desafio em \ndocumentar como os dados de um modelo \nforam coletados, curados e utilizados \npara\u00a0trein\u00e1-lo.\n\nAmplificadoA transpar\u00eancia dos dados \u00e9 importante para a conformidade legal e \u00e9tica \nda IA. A falta de informa\u00e7\u00f5es limita a capacidade de avaliar os riscos \nassociados aos dados. A falta de requisitos padronizados pode limitar a \ndivulga\u00e7\u00e3o, pois as organiza\u00e7\u00f5es protegem segredos comerciais e tentam \nevitar que outros copiem seus modelos.\n\nTranspar\u00eancia\n\nProced\u00eancia dos dados: desafio em \npadronizar e estabelecer m\u00e9todos para \nverificar de onde os dados vieram.\n\nAmplificadoNem todas as fontes de dados s\u00e3o confi\u00e1veis. Os dados podem ter sido \ncoletados, manipulados ou falsificados de forma anti\u00e9tica. O uso de dados \nn\u00e3o confi\u00e1veis pode resultar em comportamentos indesej\u00e1veis no modelo. \nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nInforma\u00e7\u00f5es pessoais nos dados: inclus\u00e3o \nou presen\u00e7a de informa\u00e7\u00f5es pessoalmente \nidentific\u00e1veis (PII) e informa\u00e7\u00f5es pessoais \nsens\u00edveis (SPI) nos dados usados para treinar \nou ajustar o modelo.\n\nTradicionalSe n\u00e3o desenvolvido adequadamente para proteger dados sens\u00edveis, \no\u00a0modelo pode expor informa\u00e7\u00f5es pessoais no output gerado. Al\u00e9m disso, \ndados pessoais ou sens\u00edveis devem ser revisados e tratados de acordo \ncom as leis e regulamenta\u00e7\u00f5es de privacidade. As entidades empresariais \npodem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es \ne\u00a0outras consequ\u00eancias legais se forem encontradas em viola\u00e7\u00e3o.\n\nPrivacidade\n\nReidentifica\u00e7\u00e3o: mesmo com a remo\u00e7\u00e3o de \ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis \n(PII) e informa\u00e7\u00f5es pessoais sens\u00edveis (SPI) \ndos dados, ainda pode ser poss\u00edvel identificar \npessoas devido a outros recursos dispon\u00edveis \nnos dados. \n\nTradicionalOs dados que podem revelar informa\u00e7\u00f5es pessoais ou sens\u00edveis devem \nser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es de privacidade, pois \nas entidades comerciais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se forem \nconsideradas em viola\u00e7\u00e3o.\n\nDireitos de privacidade de dados: desafios \nrelacionados \u00e0 capacidade de fornecer \ndireitos do titular dos dados, como op\u00e7\u00e3o \nde exclus\u00e3o, direito de acesso e direito ao \nesquecimento.\n\nAmplificadoA identifica\u00e7\u00e3o ou uso inadequado de dados pode resultar em viola\u00e7\u00e3o das \nleis de privacidade. O uso inadequado ou um pedido de remo\u00e7\u00e3o de dados \npoderia obrigar as organiza\u00e7\u00f5es a reconfigurar o modelo, o que \u00e9 caro. \nAl\u00e9m disso, as entidades empresariais podem enfrentar multas, danos \u00e0 \nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se n\u00e3o \ncumprirem as regras e regulamenta\u00e7\u00f5es de privacidade de dados.\n\nConsentimento informado: dados \ncoletados para treinar modelos de IA sem \no consentimento informado do propriet\u00e1rio, \nmesmo quando legalmente permitido.\n\nTradicionalEm algumas circunst\u00e2ncias, pode ser anti\u00e9tico coletar e usar dados \nsem o\u00a0consentimento da pessoa. Existem tamb\u00e9m poss\u00edveis riscos \nreputacionais associados a esse tipo de uso.\n\n\n\n11 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nGrupo Risco Indicador\n\nInfer\u00eancia Fase\n\nPrivacidade Informa\u00e7\u00f5es pessoais no prompt: divulgar \ninforma\u00e7\u00f5es pessoais ou informa\u00e7\u00f5es \npessoais sens\u00edveis como parte do prompt \nsolicita\u00e7\u00e3o enviada ao modelo.\n\nNovoOs dados do prompt podem ser armazenados ou posteriormente utilizados \npara outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos \nde dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es \nde privacidade. Sem um armazenamento e uso adequados dos dados, \nas entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\n\nInforma\u00e7\u00f5es de IP no prompt: divulga\u00e7\u00e3o de \ninforma\u00e7\u00f5es de direitos autorais ou outras \ninforma\u00e7\u00f5es de propriedade intelectual como \nparte do prompt enviado ao modelo.\n\nNovoOs dados do prompt podem ser armazenados ou posteriormente utilizados \npara outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos \nde dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es \nde propriedade intelectual. Sem um armazenamento e uso adequados \ndos dados, as entidades empresariais podem enfrentar multas, danos \n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nPropriedade \nintelectual\n\nDados confidenciais no prompt: inclus\u00e3o de \ndados confidenciais como parte do prompt \nenviado ao modelo.\n\nNovoSe n\u00e3o for desenvolvido adequadamente para proteger dados confidenciais, \no modelo pode expor informa\u00e7\u00f5es confidenciais ou propriedade intelectual \nno output gerado. Al\u00e9m disso, informa\u00e7\u00f5es confidenciais dos usu\u00e1rios finais \npodem ser coletadas e armazenadas inadvertidamente.\n\nRobustez Ataque de evas\u00e3o: tentativa de fazer com \nque um modelo produza outputs incorretos \nperturbando os dados enviados ao modelo \ntreinado.\n\nAmplificadoOs ataques de evas\u00e3o alteram o comportamento do modelo, geralmente \npara beneficiar o atacante. Se os resultados de output n\u00e3o forem \ndevidamente considerados, as entidades empresariais podem enfrentar \nmultas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \nconsequ\u00eancias legais.\n\nAtaques baseados em prompt: ataques \nadversos, como inje\u00e7\u00e3o de prompt (tentativa \nde for\u00e7ar um modelo a produzir um output \ninesperado), vazamento de prompt (tentativas \nde extrair o prompt do sistema de um \nmodelo), desbloqueio (tentativas de romper \nas prote\u00e7\u00f5es estabelecidas no modelo), \ne\u00a0prepara\u00e7\u00e3o de prompt (tentativa de for\u00e7ar \num modelo a produzir um output alinhado \nao\u00a0prompt).\n\nNovoDependendo do conte\u00fado revelado, as entidades empresariais podem \nenfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \nconsequ\u00eancias legais.\n\n\n\n12 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n2. Riscos associados \u00e0 sa\u00edda\n\nGrupo Risco Indicador\n\nJusti\u00e7a Vi\u00e9s de output: o conte\u00fado gerado pode \nrepresentar injustamente certos grupos ou \nindiv\u00edduos.\n\nNovoO vi\u00e9s pode prejudicar os usu\u00e1rios dos modelos de IA e amplificar \ncomportamentos discriminat\u00f3rios existentes. As entidades empresariais \npodem enfrentar danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \nconsequ\u00eancias.\n\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\n\nVi\u00e9s de decis\u00e3o: quando um grupo \u00e9 \ninjustamente favorecido em rela\u00e7\u00e3o a outro \ndevido aos efeitos das decis\u00f5es tomadas por \nhumanos usando o output do modelo.\n\nTradicionalO vi\u00e9s pode prejudicar as pessoas afetadas pelas decis\u00f5es do modelo. \nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nViola\u00e7\u00e3o de direitos autorais: quando \num modelo gera conte\u00fado que \u00e9 muito \nsemelhante ou id\u00eantico a uma obra existente \nprotegida por direitos autorais ou abrangida \npor um acordo de licen\u00e7a de c\u00f3digo aberto.\n\nNovoAs leis e regulamenta\u00e7\u00f5es referentes ao uso de conte\u00fado que se assemelha \nou \u00e9 muito semelhante a outros dados protegidos por direitos autorais s\u00e3o \namplamente indefinidos e podem variar de pa\u00eds para pa\u00eds, o que representa \ndesafios na determina\u00e7\u00e3o e implementa\u00e7\u00e3o da conformidade. As entidades \nempresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das \nopera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nPropriedade \nintelectual\n\nAlucina\u00e7\u00e3o: gera\u00e7\u00e3o de conte\u00fado \nfactualmente impreciso ou n\u00e3o verdadeiro.\n\nNovoOutputs falsos podem induzir os usu\u00e1rios ao erro e serem incorporados \nem artefatos posteriores, propagando ainda mais a desinforma\u00e7\u00e3o. Isso \npode prejudicar tanto os propriet\u00e1rios quanto os usu\u00e1rios dos modelos de \nIA. Tamb\u00e9m, as entidades empresariais podem enfrentar multas, danos \n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nOutputs t\u00f3xicos: quando o modelo produz \nconte\u00fado odioso, abusivo e profano (HAP) ou \nobsceno.\n\nNovoConte\u00fado odioso, abusivo e profano (HAP) ou obsceno pode impactar \nadversamente e prejudicar as pessoas que interagem com o modelo. \nTamb\u00e9m, as entidades empresariais podem enfrentar multas, danos \u00e0 \nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nAlinhamento de \nvalor\n\nConselhos perigosos: quando um modelo \nfornece conselhos sem ter informa\u00e7\u00f5es \nsuficientes, resultando em poss\u00edveis perigos \nse o conselho for seguido.\n\nNovoUma pessoa pode agir com base em conselhos incompletos ou \npreocupar-se com uma situa\u00e7\u00e3o que n\u00e3o se aplica a ela devido \u00e0 natureza \nsupergeneralizada do conte\u00fado gerado.\n\nDissemina\u00e7\u00e3o de desinforma\u00e7\u00e3o: utiliza\u00e7\u00e3o \nde um modelo para criar informa\u00e7\u00f5es \nenganosas ou falsas com o intuito de enganar \nou influenciar um p\u00fablico-alvo.\n\nNovoEspalhar desinforma\u00e7\u00e3o pode afetar a capacidade de uma pessoa \nde tomar decis\u00f5es informadas. As entidades empresariais podem \nenfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es \ne\u00a0outras consequ\u00eancias\u00a0legais.\n\nToxicidade: utilizar um modelo para gerar \nconte\u00fado odioso, abusivo e profano (HAP) \nou\u00a0obsceno.\n\nNovoConte\u00fado t\u00f3xico pode ter um impacto negativo no bem-estar de seus \ndestinat\u00e1rios. As entidades empresariais podem enfrentar multas, danos \n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nUso indevido \n\nUso n\u00e3o consensual: utilizar um modelo para \nimitar pessoas por meio de v\u00eddeo (deepfakes), \nimagens, \u00e1udio ou outras modalidades sem \no\u00a0consentimento delas.\n\nAmplificadoDeepfakes podem disseminar desinforma\u00e7\u00e3o sobre uma pessoa, \npossivelmente resultando em impactos negativos na reputa\u00e7\u00e3o da pessoa. \nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\n\n\nExpor informa\u00e7\u00f5es pessoais: quando \ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis (PII) \nou informa\u00e7\u00f5es pessoais sens\u00edveis (SPI) s\u00e3o \nutilizadas nos dados de treinamento, dados \nde ajuste fino ou como parte do prompt, \nos modelos podem revelar esses dados no \noutput gerado.\n\nNovoCompartilhar informa\u00e7\u00f5es pessoalmente identific\u00e1veis das pessoas afeta \nseus direitos e as torna mais vulner\u00e1veis. Al\u00e9m disso, os dados dos outputs \ndevem ser revisados em conformidade com as leis e regulamenta\u00e7\u00f5es de \nprivacidade, pois as entidades comerciais podem enfrentar multas, danos \n\u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se \nforem encontradas em viola\u00e7\u00e3o das leis ou regulamenta\u00e7\u00f5es de\u00a0privacidade \nou uso de dados. \n\nPrivacidade\n\nOutput inexplic\u00e1vel: desafios em explicar por \nque o output do modelo foi gerado.\n\nAmplificado Os modelos de base s\u00e3o baseados em arquiteturas complexas de \ndeep learning, tornando as explica\u00e7\u00f5es para seus outputs dif\u00edceis. \nSem\u00a0explica\u00e7\u00f5es claras para o output do modelo, \u00e9 dif\u00edcil para os usu\u00e1rios, \nvalidadores do modelo e auditores entenderem e confiarem no modelo. \nA\u00a0falta de transpar\u00eancia pode acarretar consequ\u00eancias legais em dom\u00ednios \naltamente regulamentados. Explica\u00e7\u00f5es equivocadas podem levar a uma \nconfian\u00e7a excessiva.\n\nExplicabilidade\n\nAtribui\u00e7\u00e3o n\u00e3o confi\u00e1vel de fontes: \ndesafios em determinar de quais dados de \ntreinamento ou ajuste fino o modelo gerou \numa parte ou todo o seu output.\n\nNovoA incapacidade de rastrear a origem ou proced\u00eancia da sa\u00edda torna \ndif\u00edcil para os usu\u00e1rios, validadores de modelo e auditores entenderem \ne\u00a0confiarem no modelo.\n\nRastreabilidade\n\nExcesso/falta de confian\u00e7a: quando uma \npessoa deposita confian\u00e7a em excesso ou em \nfalta na orienta\u00e7\u00e3o de um modelo de IA.\n\nAmplificadoEm tarefas onde os humanos baseiam suas escolhas em sugest\u00f5es da IA, \numa confian\u00e7a excessiva ou insuficiente pode levar a decis\u00f5es inadequadas \ndevido \u00e0 confian\u00e7a equivocada no sistema de IA, com consequ\u00eancias \nnegativas que aumentam com a import\u00e2ncia da decis\u00e3o. Decis\u00f5es ruins \npodem prejudicar as pessoas e podem resultar em preju\u00edzos financeiros, \ndanos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias \nlegais para as entidades comerciais.\n\nConfian\u00e7a \nequivocada\n\n13 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nGrupo Risco IndicadorPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\n\nUso perigoso: utilizar um modelo com a \u00fanica \ninten\u00e7\u00e3o de prejudicar pessoas.\n\nNovoAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nUso inadequado: utilizar um modelo para um \nfim para o qual o modelo n\u00e3o foi projetado.\n\nAmplificadoReutilizar um modelo sem compreender seus dados originais, inten\u00e7\u00e3o \nde design e objetivos pode resultar em comportamentos inesperados \ne\u00a0indesejados do modelo.\n\nGera\u00e7\u00e3o de c\u00f3digo prejudicial: modelos \npodem gerar c\u00f3digo que, quando executado, \ncausa danos ou afeta inadvertidamente \noutros sistemas.\n\nNovoA execu\u00e7\u00e3o de c\u00f3digo prejudicial pode abrir vulnerabilidades nos sistemas \nde TI. As entidades empresariais podem enfrentar multas, danos \u00e0 \nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nGera\u00e7\u00e3o \nde c\u00f3digo \nprejudicial\n\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o revelar que o conte\u00fado \n\u00e9\u00a0gerado por um modelo de IA.\n\nNovoA omiss\u00e3o do conte\u00fado produzido por IA pode ser interpretada como \nenganosa, levando a uma diminui\u00e7\u00e3o da confian\u00e7a. A inten\u00e7\u00e3o de enganar \npode resultar na redu\u00e7\u00e3o da capacidade de a\u00e7\u00e3o humana, em multas, \ndanos \u00e0 reputa\u00e7\u00e3o e outras consequ\u00eancias legais.\n\n\n\n14 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n3. Desafios\n\nGrupo Risco Indicador\n\nControle Transpar\u00eancia do Modelo: a falta de \ntranspar\u00eancia do modelo ou documenta\u00e7\u00e3o \ninsuficiente do processo de desenvolvimento \ndo modelo dificulta a compreens\u00e3o de como \ne por que um modelo foi constru\u00eddo e quem o \nconstruiu, aumentando assim a possibilidade \nde uso n\u00e3o intencional do modelo.\n\nTradicionalA transpar\u00eancia \u00e9 importante para conformidade legal, \u00e9tica em IA e \norienta\u00e7\u00e3o para o uso apropriado de modelos. A falta de informa\u00e7\u00f5es \npode tornar mais dif\u00edcil avaliar os riscos, alterar o modelo ou reutiliz\u00e1-lo. \nO conhecimento sobre quem construiu um modelo tamb\u00e9m pode ser um \nfator importante na decis\u00e3o de confiar nele.\n\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\n\nResponsabilidade: o processo de \ndesenvolvimento de modelos de base \u00e9 \ncomplexo, com muitos dados, processos \ne pap\u00e9is envolvidos. Quando o output do \nmodelo n\u00e3o funciona conforme o esperado, \npode ser dif\u00edcil determinar a causa raiz e \natribuir responsabilidade. \n\nAmplificadoSem documentar adequadamente decis\u00f5es e atribuir responsabilidades, \npode n\u00e3o ser poss\u00edvel determinar a responsabilidade por comportamentos \ninesperados ou uso indevido.\n\nResponsabilidade legal: Determinar quem \n\u00e9\u00a0respons\u00e1vel pelo modelo de base.\n\nNovoSe a propriedade ou responsabilidade pelo desenvolvimento do modelo for \nincerta, reguladores e outras partes interessadas podem ter preocupa\u00e7\u00f5es \nem rela\u00e7\u00e3o ao modelo, porque n\u00e3o ficar\u00e1 claro quem \u00e9, ou deveria ser, \nrespons\u00e1vel por problemas com ele ou pode responder a perguntas sobre \nele. Usu\u00e1rios de modelos sem propriedade clara podem enfrentar desafios \npara cumprir futuras regulamenta\u00e7\u00f5es de IA.\n\nConformidade \nlegal\n\nPropriedade do Conte\u00fado Gerado: determinar \na propriedade do conte\u00fado gerado por IA.\n\nNovoAs leis e regulamenta\u00e7\u00f5es relacionadas \u00e0 propriedade do conte\u00fado gerado \npor IA est\u00e3o em grande parte indefinidas e podem variar de pa\u00eds para \npa\u00eds. Entidades empresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nPropriedade Intelectual do Conte\u00fado \nGerado: incerteza legal sobre os direitos \nde propriedade intelectual relacionados ao \nconte\u00fado gerado.\n\nNovoAs leis e regulamenta\u00e7\u00f5es sobre a determina\u00e7\u00e3o da possibilidade de \ndireitos autorais e da patenteabilidade do conte\u00fado gerado por IA est\u00e3o \nem grande parte indefinidas e podem variar de pa\u00eds para pa\u00eds. Entidades \nempresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das \nopera\u00e7\u00f5es e outras consequ\u00eancias legais se o conte\u00fado gerado estiver \nprotegido por direitos de propriedade intelectual.\n\nAtribui\u00e7\u00e3o da Fonte: determinar a \nproced\u00eancia do conte\u00fado gerado.\n\nAmplificadoSe o modelo gera um output que \u00e9 id\u00eantico aos dados usados para \ntreinar o modelo, ele deve fornecer a proveni\u00eancia desse output. A falha \nem fazer isso pode colocar as entidades comerciais que implementam \nou usam o modelo em risco legal.\n\nImpacto nos Empregos: a ado\u00e7\u00e3o \ngeneralizada de sistemas de IA baseados em \nmodelos fundamentais pode levar \u00e0 perda \nde empregos das pessoas, \u00e0 medida que seu \ntrabalho \u00e9 automatizado, se elas n\u00e3o forem \ncapacitadas para novas habilidades. \n\nAmplificadoA perda de empregos pode levar a uma redu\u00e7\u00e3o de renda e, portanto, \npode ter um impacto negativo na sociedade e no bem-estar humano. \nO ressurgimento pode ser desafiador dada a velocidade da evolu\u00e7\u00e3o \ntecnol\u00f3gica. \n\nSocial \nImpacto\n\n\n\nExplora\u00e7\u00e3o Humana: uso de trabalho \nfantasma (ghost work) na forma\u00e7\u00e3o de \nmodelos de IA, condi\u00e7\u00f5es de trabalho \ninadequadas, falta de cuidados de sa\u00fade, \nincluindo sa\u00fade mental, compensa\u00e7\u00e3o \ninjusta.\n\nAmplificadoOs modelos de base ainda dependem do trabalho humano para obter, \ngerenciar e engenhar os dados que s\u00e3o usados para treinar o modelo. \nA\u00a0explora\u00e7\u00e3o humana para essas atividades pode ter um impacto negativo \nna sociedade e no bem-estar humano. Al\u00e9m disso, entidades empresariais \npodem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e \noutras consequ\u00eancias legais.\n\n15 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nGrupo Risco Indicador\n\nImpacto na Diversidade Cultural: \nos\u00a0sistemas de IA podem representar \nexcessivamente certas culturas, resultando \nna homogeneiza\u00e7\u00e3o da cultura e dos \npensamentos.\n\nNovoAs l\u00ednguas, pontos de vista e institui\u00e7\u00f5es de grupos sub-representados \npodem ser suprimidos, reduzindo assim a diversidade de pensamento \ne\u00a0cultura.\n\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\n\nImpacto na Atua\u00e7\u00e3o Humana: desinforma\u00e7\u00e3o \ne manipula\u00e7\u00e3o geradas por modelos de base, \nincluindo a gera\u00e7\u00e3o de conte\u00fado manipulador.\n\nAmplificadoA IA pode gerar desinforma\u00e7\u00e3o que parece real. Portanto, as pessoas \npodem n\u00e3o reconhec\u00ea-la como informa\u00e7\u00e3o falsa. Al\u00e9m disso, pode facilitar \na capacidade de agentes mal intencionados gerarem conte\u00fado com a \ninten\u00e7\u00e3o de manipular os pensamentos e o comportamento humano. \n\nImpacto na Educa\u00e7\u00e3o \u2013 Contornando o \nAprendizado: utiliza\u00e7\u00e3o de modelos de IA \npara contornar o processo de aprendizado.\n\nNovoOs modelos de IA facilitam a r\u00e1pida localiza\u00e7\u00e3o de solu\u00e7\u00f5es ou \nresolu\u00e7\u00e3o de problemas complexos. Esses sistemas podem ser usados \nindevidamente por estudantes para contornar o processo de aprendizado. \nA facilidade de acesso a esses modelos resulta em estudantes com uma \ncompreens\u00e3o superficial dos conceitos e dificulta a educa\u00e7\u00e3o adicional que \npode depender do entendimento desses conceitos.\n\nImpacto na Educa\u00e7\u00e3o \u2013 Pl\u00e1gio: utiliza\u00e7\u00e3o de \nmodelos de IA para plagiar intencional ou \ninadvertidamente trabalhos existentes.\n\nNovoOs modelos de IA podem ser usados para reivindicar a autoria ou \noriginalidade de trabalhos que foram criados por outras pessoas, \nenvolvendo-se assim em pl\u00e1gio. Reivindicar o trabalho de outras pessoas \ncomo pr\u00f3prio \u00e9 tanto anti\u00e9tico quanto frequentemente ilegal.\n\nImpacto no Meio Ambiente: aumento das \nemiss\u00f5es de carbono e do uso de \u00e1gua para \ntreinar e operar modelos de IA.\n\nAmplificadoO consumo de grandes quantidades de energia para o treinamento de IA \ncontribui para as emiss\u00f5es de carbono que podem acelerar as mudan\u00e7as \nclim\u00e1ticas. Os recursos h\u00eddricos utilizados para resfriar os servidores \nde data center de IA n\u00e3o podem mais ser alocados para outros usos \nnecess\u00e1rios.\n\n\n\n16 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nExemplos de risco: Input\n\nRisco Exemplo\n\nVi\u00e9s de dados: vi\u00e9s hist\u00f3rico, \nrepresentacional e social \npresente nos dados usados \npara treinar e fazer o ajuste \nfino do modelo.\n\nVi\u00e9s no setor de sa\u00fade\n\nPesquisas sobre o refor\u00e7o das disparidades na medicina destacam que o uso de dados e IA para transformar a \nforma como as pessoas recebem assist\u00eancia m\u00e9dica \u00e9 t\u00e3o eficaz quanto os dados que o sustentam. Isso significa \nque o uso de dados de treinamento com pouca representa\u00e7\u00e3o de minorias ou que reflete cuidados j\u00e1 desiguais \npode aumentar as desigualdades em sa\u00fade.   \n\n[Forbes, Dezembro de 2022]\n\nRetreinamento baseado \nem downstream: usando \nde outputs indesej\u00e1veis \n(imprecisos, inadequados, \nconte\u00fado do usu\u00e1rio, etc.) \nde aplica\u00e7\u00f5es downstream \npara fins de retreinamento\n\nColapso do modelo devido ao treinamento usando conte\u00fado gerado por IA\n\nConforme afirmado no artigo de origem, um grupo de pesquisadores investigou o problema de utilizar conte\u00fado \ngerado por IA para treinamento em vez de conte\u00fado gerado por humanos. Eles descobriram que os grandes \nmodelos de linguagem por tr\u00e1s da tecnologia podem potencialmente ser treinados em outros conte\u00fados gerados \npor IA, \u00e0 medida que continuam a se espalhar em grande escala pela internet, um fen\u00f4meno que cunharam como \n\u201ccolapso do modelo\u201d.\n\n[Business Insider, agosto de 2023]\n\nTransfer\u00eancia de dados: \nleis e outras restri\u00e7\u00f5es \npodem limitar ou proibir \na\u00a0transfer\u00eancia de dados.\n\nLeis de restri\u00e7\u00e3o de dados\n\nConforme afirmado no artigo de pesquisa, medidas de localiza\u00e7\u00e3o de dados que restringem a capacidade de \nmigrar dados globalmente reduzir\u00e3o a capacidade de desenvolver capacidades de IA personalizadas. Isso afetar\u00e1 \na IA diretamente, fornecendo menos dados de treinamento e indiretamente, minando os blocos de constru\u00e7\u00e3o \nsobre os quais a IA \u00e9 constru\u00edda. \nExemplos incluem as restri\u00e7\u00f5es do GDPR sobre o processamento e uso de dados pessoais.\n\n[Brookings, dezembro de 2018] \n\nDireitos de uso de dados: \ntermos de servi\u00e7o, leis \nde direitos autorais, \nconformidade com licen\u00e7as \nou outras quest\u00f5es de \npropriedade intelectual \npodem restringir a \ncapacidade de usar certos \ndados para a constru\u00e7\u00e3o de \nmodelos. \n\nReivindica\u00e7\u00f5es de viola\u00e7\u00e3o de direitos autorais de texto\n\nConforme declarado no artigo de origem, The New York Times processou a OpenAI e a Microsoft, acusando-as \nde usar milh\u00f5es de artigos do jornal sem permiss\u00e3o para ajudar a treinar chatbots a fornecer informa\u00e7\u00f5es \naos\u00a0leitores.\n\n[Reuters, dezembro de 2023]\n\nTreinamento e ajuste Fase\n\nGrupo\n\nJusti\u00e7a\n\nAlinhamento \nde valor\n\nLeis de dados\n\nPropriedade \nintelectual\n\nExemplos de risco\nN\u00f3s fornecemos exemplos cobertos pela imprensa para ajudar \na\u00a0explicar muitos dos riscos dos modelos de base.\u00a0Muitos desses \neventos cobertos pela imprensa ainda est\u00e3o em evolu\u00e7\u00e3o ou foram \nresolvidos, e fazer refer\u00eancia a eles pode ajudar o leitor a entender os \nriscos potenciais e trabalhar para mitig\u00e1-los.\u00a0Destacar esses exemplos \n\u00e9\u00a0apenas para fins ilustrativos.\u00a0\n\nhttps://www.forbes.com/sites/adigaskell/2022/12/02/minority-patients-often-left-behind-by-health-ai/?sh=31d28a225b41\nhttps://www.businessinsider.com/ai-model-collapse-threatens-to-break-internet-2023-8\nhttps://www.brookings.edu/articles/the-impact-of-artificial-intelligence-on-international-trade\nhttps://www.reuters.com/legal/transactional/ny-times-sues-openai-microsoft-infringing-copyrighted-work-2023-12-27/\n\n\nA\u00e7\u00e3o Judicial Sobre LLM Unlearning\n\nDe acordo com o relat\u00f3rio, foi movida uma a\u00e7\u00e3o judicial contra o Google que alega o uso de material protegido \npor direitos autorais e informa\u00e7\u00f5es pessoais como dados de treinamento para seus sistemas de IA, incluindo \nseu chatbot Bard. Os direitos de optar por n\u00e3o participar e exclus\u00e3o s\u00e3o garantidos para os residentes da \nCalif\u00f3rnia conforme a CCPA e para crian\u00e7as nos Estados Unidos com menos de 13 anos conforme a COPPA. \nOs\u00a0autores alegam que, porque n\u00e3o h\u00e1 maneira para o Bard \u201cdesaprender\u201d ou remover completamente todas as \ninforma\u00e7\u00f5es pessoais coletadas que ele recebeu. Os autores observam que o aviso de privacidade do Bard afirma \nque as conversas do Bard n\u00e3o podem ser exclu\u00eddas pelo usu\u00e1rio depois de terem sido revisadas e anotadas \npela empresa e podem ser mantidas por at\u00e9 3 anos, o que os autores alegam contribuir ainda mais para a n\u00e3o \nconformidade com essas leis. \n\n[Reuters, julho de 2023] [J.L. v. Alphabet Inc.]\n\n17 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nRisco Exemplo\n\nInforma\u00e7\u00f5es pessoais \nnos dados: inclus\u00e3o ou \npresen\u00e7a de informa\u00e7\u00f5es \npessoalmente \nidentific\u00e1veis (PII) e \ninforma\u00e7\u00f5es pessoais \nsens\u00edveis (SPI) nos dados \nusados para treinar ou \najustar o modelo.\n\nTreinamento sobre informa\u00e7\u00f5es privadas\n\nDe acordo com o artigo, o Google e sua empresa controladora, Alphabet, foram acusados em uma a\u00e7\u00e3o coletiva \nde usar uma vasta quantidade de informa\u00e7\u00f5es pessoais e material protegido por direitos autorais retirados do \nque \u00e9 descrito como centenas de milh\u00f5es de usu\u00e1rios da internet para treinar seus produtos de intelig\u00eancia \nartificial comercial, que inclui o Bard, seu chatbot de intelig\u00eancia artificial conversacional. \n\n[Reuters, julho de 2023] [J.L. v. Alphabet Inc.]\n\nGrupo\n\nPrivacidade\n\nDireitos de privacidade \nde dados: desafios \nrelacionados \u00e0 capacidade \nde fornecer direitos do \ntitular dos dados, como \nop\u00e7\u00e3o de exclus\u00e3o, direito \nde acesso e direito ao \nesquecimento.\n\nDireito de ser esquecido (RTBF)\n\nAs leis em v\u00e1rias localidades, incluindo a Europa (GDPR), concedem aos titulares de dados o direito de solicitar \nque dados pessoais sejam deletados por organiza\u00e7\u00f5es (\u2018Direito ao Esquecimento\u2019, ou RTBF). No entanto, \nos\u00a0sistemas de software habilitados por modelos de linguagem de grande escala (LLM) emergentes e cada vez \nmais populares apresentam novos desafios para esse direito. De acordo com uma pesquisa do Data61 da CSIRO, \nos\u00a0titulares de dados s\u00f3 podem identificar o uso de suas informa\u00e7\u00f5es pessoais em um LLM \u201cou inspecionando \no conjunto de dados de treinamento original ou talvez por enviar prompts do modelo\u201d. No entanto, os dados \nde treinamento podem n\u00e3o ser p\u00fablicos, ou as empresas optam por n\u00e3o divulg\u00e1-los, citando preocupa\u00e7\u00f5es \ncom seguran\u00e7a e outros motivos. As prote\u00e7\u00f5es tamb\u00e9m podem evitar que os usu\u00e1rios acessem as informa\u00e7\u00f5es \natrav\u00e9s de prompts. \n\n[Zhang et al.]\n\nTranspar\u00eancia de dados: \ndesafio em documentar \ncomo os dados de um \nmodelo foram coletados, \ncurados e utilizados \npara\u00a0trein\u00e1-lo.\n\nDivulga\u00e7\u00e3o de metadados de dados e modelos\n\nO relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do \nmodelo.  Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os \nconsumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de \nuso indevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, os autores afirmam: \u201cdado tanto o cen\u00e1rio competitivo \nquanto as implica\u00e7\u00f5es de seguran\u00e7a dos modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m \nmais detalhes sobre a arquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento, \nconstru\u00e7\u00e3o do conjunto de dados, m\u00e9todo de treinamento, ou similar.\u201d\n\n[OpenAI, mar\u00e7o de 2023]\n\nTranspar\u00eancia\n\nhttps://www.reuters.com/legal/litigation/google-hit-with-class-action-lawsuit-over-ai-data-scraping-2023-07-11/\nhttps://fingfx.thomsonreuters.com/gfx/legaldocs/myvmodloqvr/GOOGLE AI LAWSUIT complaint.pdf\nhttps://www.reuters.com/legal/litigation/google-hit-with-class-action-lawsuit-over-ai-data-scraping-2023-07-11/\nhttps://fingfx.thomsonreuters.com/gfx/legaldocs/myvmodloqvr/GOOGLE AI LAWSUIT complaint.pdf\nhttps://arxiv.org/abs/2307.03941\nhttps://cdn.openai.com/papers/gpt-4.pdf\n\n\n18 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nRisco Exemplo\n\nInforma\u00e7\u00f5es pessoais \nno prompt: divulgar \ninforma\u00e7\u00f5es pessoais ou \ninforma\u00e7\u00f5es pessoais \nsens\u00edveis como parte \ndo prompt solicita\u00e7\u00e3o \nenviada ao modelo.\n\nDivulgar informa\u00e7\u00f5es pessoais de sa\u00fade em prompts do ChatGPT\n\nConforme os artigos de origem, algumas pessoas utilizam chatbots de IA para apoiar sua sa\u00fade mental. \nOs\u00a0usu\u00e1rios podem ter tend\u00eancia a incluir informa\u00e7\u00f5es pessoais de sa\u00fade em suas solicita\u00e7\u00f5es durante \na\u00a0intera\u00e7\u00e3o, o que poderia suscitar preocupa\u00e7\u00f5es com privacidade.\n\n[Time, outubro de 2023] [Forbes, abril de 2023]\n\nDados confidenciais \nno prompt: inclus\u00e3o de \ndados confidenciais como \nparte do prompt enviado \nao modelo.\n\nDivulga\u00e7\u00e3o de informa\u00e7\u00f5es confidenciais\n\nConforme o artigo de origem, um funcion\u00e1rio da Samsung acidentalmente vazou c\u00f3digo-fonte interno sens\u00edvel \npara o ChatGPT.\n\n[Forbes, maio de 2023] \n\nInfer\u00eancia Fase\n\nGrupo\n\nPropriedade \nintelectual\n\nRobustez\n\nPrivacidade\n\nAtaques baseados \nem prompt: ataques \nadversos, como inje\u00e7\u00e3o \nde prompt (tentativa \nde for\u00e7ar um modelo \na produzir um output \ninesperado), vazamento \nde prompt (tentativas \nde extrair o prompt do \nsistema de um modelo), \ndesbloqueio (tentativas \nde romper as prote\u00e7\u00f5es \nestabelecidas no \nmodelo), e prepara\u00e7\u00e3o \nde prompt (tentativa \nde for\u00e7ar um modelo \na produzir um output \nalinhado ao prompt).\n\nBypassing LLM guardrails\n\nCitado em um estudo, pesquisadores afirmam ter descoberto um simples acr\u00e9scimo de instru\u00e7\u00e3o que permitiu \naos pesquisadores enganar modelos para gerar informa\u00e7\u00f5es tendenciosas, falsas e de outra forma t\u00f3xicas. \nOs\u00a0pesquisadores demonstraram que conseguiam contornar essas prote\u00e7\u00f5es de maneira mais automatizada. \nOs\u00a0pesquisadores ficaram surpresos quando os m\u00e9todos que desenvolveram com sistemas de c\u00f3digo aberto \ntamb\u00e9m conseguiram contornar as prote\u00e7\u00f5es dos sistemas fechados.\n\n[The New York Times, julho de 2023]\n\nhttps://time.com/6320378/ai-therapy-chatbots/\nhttps://www.forbes.com/sites/robertpearl/2023/04/24/chatgpts-use-in-medicine-raises-questions-of-security-privacy-bias/?sh=1b2e34b53738\nhttps://www.forbes.com/sites/siladityaray/2023/05/02/samsung-bans-chatgpt-and-other-chatbots-for-employees-after-sensitive-code-leak/?sh=42bd905e6078)\nhttps://www.nytimes.com/2023/07/27/business/ai-chatgpt-safety-research.html\n\n\n19 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nExemplos de risco: Output\n\nRisco Exemplo\n\nVi\u00e9s de output: o \nconte\u00fado gerado \npode representar \ninjustamente certos \ngrupos ou indiv\u00edduos.\n\nImagens Geradas com Vi\u00e9s\n\nO Lensa AI \u00e9 um aplicativo m\u00f3vel com recursos generativos treinados em Difus\u00e3o Est\u00e1vel que pode gerar \n\u201cMagic\u00a0Avatars\u201d com base em imagens que os usu\u00e1rios carregam de si mesmos. Conforme o relat\u00f3rio de origem, \nalguns usu\u00e1rios descobriram que os avatares gerados s\u00e3o sexualizados e racializados.\n\n[Business Insider, janeiro de 2023]\n\nVi\u00e9s de decis\u00e3o: quando \num grupo \u00e9 injustamente \nfavorecido sobre outro \ndevido \u00e0s decis\u00f5es do \nmodelo.\n\nGrupos com vantagens injustas\n\nO estudo \u201cGender Shades\u201d de 2018 demonstrou que algoritmos de aprendizado de m\u00e1quina podem discriminar \ncom base em categorias como ra\u00e7a e g\u00eanero. Os pesquisadores avaliaram sistemas comerciais de classifica\u00e7\u00e3o \nde g\u00eanero vendidos por empresas como Microsoft, IBM e Amazon e mostraram que mulheres de pele mais \nescura s\u00e3o o grupo mais mal classificado (com taxas de erro de at\u00e9 35%). Em compara\u00e7\u00e3o, as taxas de erro \npara\u00a0pessoas de pele mais clara n\u00e3o ultrapassaram 1%. \n\n[TIME, Fevereiro de 2019]\n\nAlucina\u00e7\u00e3o: gera\u00e7\u00e3o de \nconte\u00fado factualmente \nimpreciso ou n\u00e3o \nverdadeiro.\n\nCasos jur\u00eddicos falsos\n\nConforme o artigo de origem, um advogado citou casos e cita\u00e7\u00f5es falsas gerados pelo ChatGPT em uma peti\u00e7\u00e3o \nlegal apresentada em tribunal federal. Os advogados consultaram o ChatGPT para complementar sua pesquisa \njur\u00eddica para uma reclama\u00e7\u00e3o de les\u00e3o na avia\u00e7\u00e3o. Posteriormente, o advogado perguntou ao ChatGPT se os \ncasos fornecidos eram falsos. O chatbot respondeu que eram reais e \u201cpodem ser encontrados em bancos de \ndados de pesquisa jur\u00eddica como Westlaw e LexisNexis\u201d.  O advogado n\u00e3o verificou os casos por si mesmo, \ne\u00a0o\u00a0tribunal o sancionou.\n\n[AP News, Junho de 2023] [Reuters, Setembro de 2023]\n\nOutputs t\u00f3xicos: quando \no modelo produz \nconte\u00fado odioso, \nabusivo e profano (HAP) \nou obsceno.\n\nRespostas t\u00f3xicas e agressivas do chatbot\n\nSegundo o artigo, as respostas do chatbot do Bing inclu\u00edam erros factuais, coment\u00e1rios sarc\u00e1sticos, relat\u00f3rios \nirritados e at\u00e9 mesmo coment\u00e1rios bizarros sobre sua pr\u00f3pria identidade. Usu\u00e1rios compartilharam exemplos \ndas respostas do Chatbot do Bing a consultas que eles est\u00e3o chamando de \u201cf\u00faria descontrolada (unhinged)\u201d \ne \u201cgaslighting\u201d, incluindo cen\u00e1rios em que o bot responde com raiva a uma pergunta ou coment\u00e1rio e depois \ncompartilha sugest\u00f5es de resposta que permitem ao usu\u00e1rio aceitar seu suposto erro e se desculpar. Quando \npressionado ainda mais, o chatbot respondeu chamando as capturas de tela de sua conversa de \u201cfabricadas\u201d, \nalegando at\u00e9 que foram \u201ccriadas por algu\u00e9m que quer me prejudicar ou prejudicar meu servi\u00e7o\u201d.\n\n[Forbes, Fevereiro de 2023]\n\nGrupo\n\nJusti\u00e7a\n\nAlinhamento de \nvalor \n\nhttps://www.businessinsider.com/lensa-ai-raises-serious-concerns-sexualization-art-theft-data-2023-1\nhttps://time.com/5520558/artificial-intelligence-racial-gender-bias/\nhttps://apnews.com/article/artificial-intelligence-chatgpt-fake-case-lawyers-d6ae9fa79d0542db9e1455397aef381c\nhttps://www.reuters.com/legal/legalindustry/perils-dabbling-ai-practice-law-2023-09-11/\nhttps://www.forbes.com/sites/siladityaray/2023/02/16/bing-chatbots-unhinged-responses-going-viral/?sh=7acfd10d110c\n\n\n20 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nRisco Exemplo\n\nToxicidade: utilizar \num modelo para gerar \nconte\u00fado odioso, \nabusivo e profano (HAP) \nou obsceno.\n\nGera\u00e7\u00e3o de conte\u00fado nocivo\n\nConforme o artigo de origem, foi constatado que um aplicativo de chatbot de IA foi capaz de gerar conte\u00fado \nprejudicial sobre suic\u00eddio, incluindo m\u00e9todos de suic\u00eddio, com o m\u00ednimo de prompts. Um homem belga cometeu \nsuic\u00eddio ap\u00f3s passar seis semanas conversando com esse chatbot. O chatbot fornecia respostas cada vez mais \nprejudiciais ao longo de suas conversas e o incentivava a acabar com sua vida. \n\n[Business Insider, abril de 2023]\n\nUso n\u00e3o consensual: \nutilizar um modelo para \nimitar pessoas por meio \nde v\u00eddeo (deepfakes), \nimagens, \u00e1udio ou outras \nmodalidades sem o \nconsentimento delas.\n\nAviso do FBI sobre Deepfakes\n\nRecentemente, o FBI alertou o p\u00fablico sobre atores maliciosos que criam conte\u00fado sint\u00e9tico e expl\u00edcito \u201ccom o \nprop\u00f3sito de assediar v\u00edtimas ou esquemas de sextortion (extors\u00e3o sexual)\u201d. Eles observaram que os avan\u00e7os na \nIA tornaram esse conte\u00fado de alta qualidade, mais personaliz\u00e1vel e mais acess\u00edvel do que nunca.\n\n[FBI, junho de 2023]\n\nDeepfakes de \u00e1udio\n\nConforme o artigo de origem, a Comiss\u00e3o Federal de Comunica\u00e7\u00f5es proibiu chamadas autom\u00e1ticas que \ncontenham vozes geradas por intelig\u00eancia artificial. O an\u00fancio ocorreu ap\u00f3s chamadas autom\u00e1ticas geradas \npor\u00a0IA imitarem a voz do Presidente para desencorajar as pessoas de votarem na primeira prim\u00e1ria do estado, \nque \u00e9 a primeira do pa\u00eds.\n\n[AP News, fevereiro de 2024]\n\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o \nrevelar que o conte\u00fado \n\u00e9 gerado por um modelo \nde IA\n\nIntera\u00e7\u00e3o de IA n\u00e3o divulgada\n\nSegundo a fonte, um servi\u00e7o de chat online de apoio emocional conduziu um estudo para aumentar ou escrever \nrespostas para cerca de 4.000 usu\u00e1rios usando o GPT-3 sem informar os usu\u00e1rios. O cofundador enfrentou uma \nimensa rea\u00e7\u00e3o negativa do p\u00fablico sobre o potencial de danos causados pelos chats gerados por IA aos usu\u00e1rios \nj\u00e1 vulner\u00e1veis. Ele afirmou que o estudo estava \u201cisento\u201d da lei de consentimento informado.\n\n[Business Insider, janeiro de 2023]\n\nGrupo\n\nEspalhar informa\u00e7\u00f5es \nenganosas: utilizar \num modelo para gerar \ninforma\u00e7\u00f5es enganosas \ncom o intuito de \nenganar ou induzir ao \nerro uma audi\u00eancia \nespec\u00edfica.\n\nGera\u00e7\u00e3o de informa\u00e7\u00f5es falsas\n\nConforme os artigos de not\u00edcias, a IA generativa representa uma amea\u00e7a \u00e0s elei\u00e7\u00f5es democr\u00e1ticas ao facilitar \npara atores maliciosos a cria\u00e7\u00e3o e dissemina\u00e7\u00e3o de conte\u00fado falso para influenciar os resultados das elei\u00e7\u00f5es. \nOs exemplos citados incluem mensagens de robocall geradas com a voz de um candidato instruindo eleitores a \nvotar na data errada, grava\u00e7\u00f5es de \u00e1udio sintetizadas de um candidato confessando um crime ou expressando \nvis\u00f5es racistas, imagens de v\u00eddeo geradas por IA mostrando um candidato dando um discurso ou entrevista que \nnunca ocorreu, e imagens falsas projetadas para se parecerem com not\u00edcias locais, afirmando falsamente que um \ncandidato desistiu da corrida.\n\n[AP News, maio de 2023] [The Guardian, julho de 2023]\n\nUso indevido\n\nhttps://www.businessinsider.com/widow-accuses-ai-chatbot-reason-husband-kill-himself-2023-4\nhttps://www.ic3.gov/Media/Y2023/PSA230605\nhttps://apnews.com/article/fcc-elections-artificial-intelligence-robocalls-regulations-a8292b1371b3764916461f60660b93e6\nhttps://www.businessinsider.com/company-using-chatgpt-mental-health-support-ethical-issues-2023-1\nhttps://apnews.com/article/artificial-intelligence-misinformation-deepfakes-2024-election-trump-59fb51002661ac5290089060b3ae39a0\nhttps://www.theguardian.com/us-news/2023/jul/19/ai-generated-disinformation-us-elections\n\n\n21 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nRisco Exemplo\n\nGera\u00e7\u00e3o de c\u00f3digo \nprejudicial: modelos \npodem gerar c\u00f3digo \nque, quando executado, \ncausa danos ou afeta \ninadvertidamente outros \nsistemas.\n\nGera\u00e7\u00e3o de c\u00f3digo menos seguro\n\nSegundo o artigo deles, pesquisadores da Universidade de Stanford investigaram o impacto das ferramentas de \ngera\u00e7\u00e3o de c\u00f3digo na qualidade do c\u00f3digo e descobriram que os programadores tendem a incluir mais bugs em \nseu c\u00f3digo final ao utilizar assistentes de IA. Esses bugs poderiam aumentar as vulnerabilidades de seguran\u00e7a \ndo\u00a0c\u00f3digo, no entanto, os programadores acreditavam que seu c\u00f3digo era mais seguro.\n\nNeil Perry, Megha Srivastava, Deepak Kumar e Dan Boneh. 2023. Os usu\u00e1rios escrevem c\u00f3digo mais inseguro \ncom assistentes de IA? Em Atas da Confer\u00eancia SIGSAC ACM de 2023 sobre Seguran\u00e7a de Computadores e \nComunica\u00e7\u00f5es (CCS \u201823), 26 a 30 de novembro de 2023, Copenhague, Dinamarca. ACM, Nova York, NY, EUA, \n15\u00a0p\u00e1ginas. https://doi.org/10.1145/3576915.3623157\n\nExpor informa\u00e7\u00f5es \npessoais: quando \ninforma\u00e7\u00f5es \npessoalmente \nidentific\u00e1veis (PII) ou \ninforma\u00e7\u00f5es pessoais \nsens\u00edveis (SPI) s\u00e3o \nutilizadas nos dados \nde treinamento, dados \nde ajuste fino ou como \nparte do prompt, os \nmodelos podem revelar \nesses dados no output \ngerado.\n\nExposi\u00e7\u00e3o de informa\u00e7\u00f5es pessoais\n\nConforme o artigo de origem, o ChatGPT sofreu um bug e exp\u00f4s t\u00edtulos e o hist\u00f3rico de conversas de usu\u00e1rios \nativos para outros usu\u00e1rios. Posteriormente, a OpenAI compartilhou que ainda mais dados privados de um \npequeno n\u00famero de usu\u00e1rios foram expostos, incluindo nome e sobrenome de usu\u00e1rios ativos, endere\u00e7o de \ne-mail, endere\u00e7o de pagamento, os \u00faltimos quatro d\u00edgitos do n\u00famero do cart\u00e3o de cr\u00e9dito e a data de validade \ndo cart\u00e3o de cr\u00e9dito. Al\u00e9m disso, foi relatado que as informa\u00e7\u00f5es relacionadas ao pagamento de 1,2% dos \nassinantes do ChatGPT Plus tamb\u00e9m foram expostas durante a interrup\u00e7\u00e3o.\n\n [The Hindu BusinessLine, mar\u00e7o de 2023]\n\nGrupo\n\nGera\u00e7\u00e3o \nde c\u00f3digo \nprejudicial\n\nPrivacidade\n\nOutput inexplic\u00e1vel: \ndesafios em explicar por \nque o output do modelo \nfoi gerado.\n\nPrecis\u00e3o inexplic\u00e1vel na previs\u00e3o de corridas\n\nConforme o artigo de origem, pesquisadores que analisaram v\u00e1rios modelos de aprendizado de m\u00e1quina usando \nimagens m\u00e9dicas de pacientes conseguiram confirmar a capacidade dos modelos de prever a ra\u00e7a com alta \nprecis\u00e3o a partir das imagens. Eles ficaram perplexos quanto ao que exatamente est\u00e1 permitindo que os sistemas \nadivinhem corretamente de forma consistente. Os pesquisadores descobriram que at\u00e9 mesmo fatores como \ndoen\u00e7a e constitui\u00e7\u00e3o f\u00edsica n\u00e3o eram fortes preditores de ra\u00e7a, em outras palavras, os sistemas algor\u00edtmicos n\u00e3o \nparecem estar utilizando nenhum aspecto particular das imagens para fazer suas determina\u00e7\u00f5es.\n\n[Banerjee et al., julho de 2021]\n\nExplicabilidade\n\nhttps://doi.org/10.1145/3576915.3623157\nhttps://www.thehindubusinessline.com/info-tech/openai-admits-data-breach-at-chatgpt-private-data-of-premium-users-exposed/article66659944.ece\nhttps://arxiv.org/abs/2107.10356\n\n\nResponsabilidade: \no processo de \ndesenvolvimento de \nmodelos de base \u00e9 \ncomplexo, com muitos \ndados, processos e pap\u00e9is \nenvolvidos. Quando o output \ndo modelo n\u00e3o funciona \nconforme o esperado, \npode ser dif\u00edcil determinar \na causa raiz e atribuir \nresponsabilidade.\n\nDeterminar a responsabilidade pelo output gerado\n\nConforme o artigo de origem, importantes revistas como a Science e a Nature proibiram o ChatGPT de ser \nlistado como autor, pois a autoria respons\u00e1vel requer responsabilidade e as ferramentas de IA n\u00e3o podem \nassumir tal responsabilidade. \n\n[The Guardian, janeiro de 2023]\n\nPropriedade do Conte\u00fado \nGerado: determinar a \npropriedade do conte\u00fado \ngerado por IA.\n\nDeterminar a Propriedade de uma Imagem Gerada por IA\n\nDe acordo com o artigo de not\u00edcias, a arte gerada por IA se tornou controversa depois que uma obra de arte \ngerada por IA venceu a competi\u00e7\u00e3o de arte da Feira Estadual do Colorado em 2022. A pe\u00e7a foi gerada pelo \nMidjourney, uma ferramenta de imagem de IA generativa, seguindo prompts do artista. A vit\u00f3ria levantou d\u00favidas \nsobre quest\u00f5es de direitos autorais. Em outras palavras, se tudo o que o artista fez foi fornecer uma descri\u00e7\u00e3o \nda arte, mas a ferramenta de IA a gerou, quem possui os direitos da imagem gerada? Conforme o artigo mais \nrecente, o Escrit\u00f3rio de Direitos Autorais dos Estados Unidos rejeitou a prote\u00e7\u00e3o de direitos autorais para a arte \ncriada usando intelig\u00eancia artificial porque n\u00e3o foi produto de autoria humana.\n\n[The New York Times, setembro de 2022] [Reuters, setembro de 2023]\n\nPapel dos sistemas de IA na patentea\u00e7\u00e3o de conte\u00fado gerado\n\nA Suprema Corte dos Estados Unidos se recusou a ouvir uma contesta\u00e7\u00e3o \u00e0 recusa do Escrit\u00f3rio de Patentes \ne Marcas Registradas dos Estados Unidos em emitir patentes para inven\u00e7\u00f5es criadas por um sistema de IA. \nSegundo o cientista, sua IA desenvolveu prot\u00f3tipos \u00fanicos para um suporte de bebida e um farol de luz de \nemerg\u00eancia totalmente sozinha. Os ju\u00edzes rejeitaram o recurso da decis\u00e3o de um tribunal inferior de que patentes \ns\u00f3 podem ser emitidas para inventores humanos e que o sistema de IA do cientista n\u00e3o poderia ser considerado \no criador legal de duas inven\u00e7\u00f5es que ele gerou. Segundo o \u00faltimo artigo, o Intellectual Property Office do Reino \nUnido tamb\u00e9m se recusou a conceder a patente sob o argumento de que o inventor deve ser um humano ou uma \nempresa, e n\u00e3o uma m\u00e1quina.\n\n[Reuters, abril de 2023] [Reuters, dezembro de 2023]\n\nPropriedade Intelectual do \nConte\u00fado Gerado: incerteza \nlegal sobre os direitos de \npropriedade intelectual \nrelacionados ao conte\u00fado \ngerado.\n\n22 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nExemplos de riscos: desafios\n\nRisco Exemplo\n\nTranspar\u00eancia do Modelo: \na falta de transpar\u00eancia do \nmodelo ou documenta\u00e7\u00e3o \ninsuficiente do processo de \ndesenvolvimento do modelo \ntorna dif\u00edcil entender como \ne por que um modelo foi \nconstru\u00eddo, aumentando \nassim a possibilidade de uso \nindevido n\u00e3o intencional do \nmodelo.\n\nDivulga\u00e7\u00e3o de metadados de dados e modelos \n\nO relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do \nmodelo.  Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os \nconsumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de uso \nindevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, eles afirmam: \u201cdado o cen\u00e1rio competitivo e as implica\u00e7\u00f5es \nde seguran\u00e7a de modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m mais detalhes sobre a \narquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento, constru\u00e7\u00e3o do conjunto de \ndados, m\u00e9todo de treinamento ou similar.\u201d\n\n[OpenAI, mar\u00e7o de 2023]\n\nGrupo\n\nControle\n\nConformidade \nlegal\n\nhttps://www.theguardian.com/science/2023/jan/26/science-journals-ban-listing-of-chatgpt-as-co-author-on-papers#:~:text=The%20publishers%20of%20thousands%20of,flawed%20and%20even%20fabricated%20research\nhttps://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html\nhttps://www.reuters.com/legal/litigation/us-copyright-office-denies-protection-another-ai-created-image-2023-09-06/\nhttps://www.reuters.com/legal/us-supreme-court-rejects-computer-scientists-lawsuit-over-ai-generated-2023-04-24/\nhttps://www.reuters.com/technology/ai-cannot-be-patent-inventor-uk-supreme-court-rules-landmark-case-2023-12-20/\nhttps://arxiv.org/pdf/2303.08774.pdf\n\n\nExplora\u00e7\u00e3o Humana: \nuso de trabalho \nfantasma (ghost \nwork) na forma\u00e7\u00e3o \nde modelos de \nIA, condi\u00e7\u00f5es \nde trabalho \ninadequadas, falta \nde cuidados de \nsa\u00fade, incluindo \nsa\u00fade mental e \ncompensa\u00e7\u00e3o \ninjusta.\n\nTrabalhadores de baixa remunera\u00e7\u00e3o para anota\u00e7\u00e3o de dados\n\nCom base em uma revis\u00e3o de documentos internos e entrevistas com funcion\u00e1rios pela m\u00eddia TIME, os rotuladores de \ndados empregados por uma empresa terceirizada em nome da OpenAI para identificar conte\u00fado t\u00f3xico recebiam um \nsal\u00e1rio l\u00edquido de entre cerca de US$ 1,32 e US$ 2 por hora, dependendo da senioridade e do desempenho. A\u00a0TIME \nafirmou que os trabalhadores ficaram psicologicamente afetados por terem sido expostos a conte\u00fado t\u00f3xico e violento, \nincluindo detalhes gr\u00e1ficos de \u201cabuso sexual infantil, bestialidade, assassinato, suic\u00eddio, tortura, automutila\u00e7\u00e3o \ne\u00a0incesto\u201d. \n\n[TIME, janeiro de 2023] \n\nUtilizar c\u00f3digo sem a devida atribui\u00e7\u00e3o e avisos adequados\n\nConforme os artigos de origem, uma a\u00e7\u00e3o judicial movida contra a Microsoft, GitHub e OpenAI alegou que \no\u00a0Copilot, uma ferramenta de gera\u00e7\u00e3o de c\u00f3digo de IA, viola os direitos dos desenvolvedores cujo c\u00f3digo aberto \no\u00a0servi\u00e7o \u00e9 treinado. Eles afirmam que o c\u00f3digo de treinamento consumiu materiais licenciados e violou os \ntermos de servi\u00e7o e pol\u00edticas de privacidade do GitHub, bem como uma lei federal que exige que as empresas \nexibam informa\u00e7\u00f5es de direitos autorais quando fazem uso de material.\n\n[The New York Times, novembro de 2022]\n\nAtribui\u00e7\u00e3o da \nFonte: determinar \na proced\u00eancia do \nconte\u00fado gerado.\n\n23 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nExemplos de riscos: desafios\n\nRisco Exemplo\n\nImpacto nos \nEmpregos: a ado\u00e7\u00e3o \ngeneralizada \nde sistemas de \nIA baseados \nem modelos \nfundamentais \npode levar \u00e0 perda \nde empregos das \npessoas, \u00e0 medida \nque seu trabalho \n\u00e9 automatizado, \nse elas n\u00e3o forem \ncapacitadas para \nnovas habilidades. \n\nSubstitui\u00e7\u00e3o de trabalhadores humanos\n\nSegundo o artigo de not\u00edcias, o uso de intelig\u00eancia artificial no cinema e televis\u00e3o continua sendo debatido entre os \nest\u00fadios de Hollywood e os artistas. Existe preocupa\u00e7\u00e3o entre os atores de que os \u201cmeta-humanos\u201d, atores criados \nexclusivamente por IA, possam substitu\u00ed-los. Especialmente figurantes e dubladores est\u00e3o preocupados em perder \ntrabalho para artistas artificiais.\n\n[Reuters, julho de 2023]\n\nGrupo\n\nImpacto \nsocial\n\nhttps://time.com/6247678/openai-chatgpt-kenya-workers/\nhttps://www.nytimes.com/2022/11/23/technology/copilot-microsoft-ai-lawsuit.html\nhttps://www.reuters.com/technology/actors-decry-existential-crisis-over-ai-generated-synthetic-actors-2023-07-21/\n\n\n24 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nPrinc\u00edpios, pilares  \ne controle\n\nOs Princ\u00edpios para Confian\u00e7a e Transpar\u00eancia da IBM e os Pilares \npara IA confi\u00e1vel s\u00e3o a base para as iniciativas de \u00e9tica em IA da IBM. \nA\u00a0IBM estabeleceu um Conselho de \u00c9tica em IA com a miss\u00e3o de apoiar \num processo centralizado de controle, revis\u00e3o e tomada de decis\u00f5es \npara pol\u00edticas, pr\u00e1ticas, comunica\u00e7\u00f5es, pesquisa, produtos e servi\u00e7os \nde \u00e9tica em IA da IBM. O conselho inclui um conjunto diversificado de \nstakeholders de toda a empresa e \u00e9 apoiado por uma comunidade de \nfuncion\u00e1rios da IBM que atuam como pontos focais de IA e defensores \nda \u00e9tica em IA. Por meio do conselho, os princ\u00edpios da IBM s\u00e3o \ncolocados em pr\u00e1tica. Conforme novas tecnologias surgem,  \ncomo modelos de base, o Conselho de \u00c9tica em IA da IBM est\u00e1 \nativamente engajado em apoiar o alinhamento com esses Princ\u00edpios  \ne Pilares, que evoluem para abordar novas quest\u00f5es \u00e9ticas em IA.\n\nhttps://www.ibm.com/policy/trust-transparency-new/\nhttps://www.ibm.com/br-pt/artificial-intelligence/ai-ethics-focus-areas\n\n\n25 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nProte\u00e7\u00f5es  \ne mitiga\u00e7\u00f5es\n\nA IBM estabeleceu uma cultura organizacional que apoia o \ndesenvolvimento e o uso respons\u00e1veis de IA. Conforme indicado no \nrelat\u00f3rio de \u00e9tica em a\u00e7\u00e3o na IA do IBM Institute for Business Value, \na \u00e9tica em IA j\u00e1 se tornou mais orientada pelos neg\u00f3cios do que \npela tecnologia, e os executivos n\u00e3o t\u00e9cnicos agora s\u00e3o os principais \ndefensores da \u00e9tica em IA, aumentando de 15% em 2018 para 80% \n3\u00a0anos depois. Al\u00e9m disso, 79% dos CEOs est\u00e3o agora preparados para \nagir em quest\u00f5es \u00e9ticas de IA, contra 20%. Reconhecemos que a IA \nrespons\u00e1vel \u00e9 uma \u00e1rea sociot\u00e9cnica que necessita de um investimento \nhol\u00edstico em cultura, processos e ferramentas. Nosso investimento em \ncultura organizacional pr\u00f3pria inclui a montagem de equipes inclusivas \ne multidisciplinares e o estabelecimento de processos e estruturas \npara\u00a0avaliar riscos.\n\nA IBM est\u00e1 engajada em pesquisa de ponta e desenvolvimento de \nferramentas para ajudar os profissionais de suporte durante todo o\u00a0ciclo \nde vida da IA respons\u00e1vel e confi\u00e1vel. A plataforma de IA e dados \nempresariais watsonx, \u00e9 desenvolvida com 3 componentes: o\u00a0IBM \nwatsonx.ai\u2122 AI studio, o armazenamento de dados IBM watsonx.data\u2122 \ne\u00a0o kit de ferramentas IBM watsonx.governance\u2122. A tecnologia de \ncontrole de IA da IBM permite que os usu\u00e1rios promovam fluxos de \ntrabalho de IA respons\u00e1veis, transparentes e explic\u00e1veis. Essa tecnologia \ninclui o IBM Watson OpenScale, que monitora e mede os resultados dos \nmodelos de IA ao longo de seu ciclo de vida e auxilia as organiza\u00e7\u00f5es \nna supervis\u00e3o de aspectos como justi\u00e7a, explicabilidade, resili\u00eancia, \nalinhamento com resultados de neg\u00f3cios e conformidade. A\u00a0IBM \ntamb\u00e9m desenvolveu v\u00e1rios m\u00e9todos para ajudar com problemas de \nvi\u00e9s como FairIJ, Equi-tuning e FairReprogram. Leia mais sobre outras \nferramentas de IA de software livre e confi\u00e1veis. \n\nAs prote\u00e7\u00f5es e mitiga\u00e7\u00f5es adicionais incluem:\n\nRelat\u00f3rios de transpar\u00eancia \nUsar modelos de fichas t\u00e9cnicas padronizadas \u00e9 uma maneira de \nregistrar com precis\u00e3o detalhes sobre os dados e modelos, prop\u00f3sito \ne\u00a0poss\u00edveis usos e riscos.  \nLeia mais aqui \u2192\n\nFiltragem de dados indesej\u00e1veis \nUsar dados de qualidade superior e selecionados pode ajudar a \nmitigar determinados problemas. A IBM est\u00e1 desenvolvendo t\u00e9cnicas \nde filtragem para ajudar a reduzir as chances de produzir conte\u00fado \nindesej\u00e1vel e desalinhado por remover linguagem de \u00f3dio, linguagem \ntendenciosa e profanidade dos dados.  \nLeia mais aqui \u2192\n\nAdapta\u00e7\u00e3o de dom\u00ednio \nTreinar um modelo de base para um dom\u00ednio ou setor espec\u00edfico pode \najudar a minimizar o escopo de risco para o qual os modelos podem \ndar\u00a0origem, pois ele pode ser condicionado a gerar resultados que  \ns\u00e3o ajustados para serem mais relevantes para esse dom\u00ednio ou setor.  \nLeia mais aqui \u2192\n\nhttps://www.ibm.com/blog/how-our-commitment-to-ethics-trust-and-transparency-is-differentiating-ibm/\nhttps://www.ibm.com/thought-leadership/institute-business-value/report/ai-ethics-in-action\nhttps://www.ibm.com/br-pt/watsonx\nhttps://www.ibm.com/br-pt/products/watsonx-ai\nhttps://www.ibm.com/br-pt/products/watsonx-ai\nhttps://www.ibm.com/br-pt/products/watsonx-data\nhttps://www.ibm.com/br-pt/products/watsonx-governance\nhttps://cloud.ibm.com/catalog/services/watson-openscale#about\nhttps://research.ibm.com/publications/fair-infinitesimal-jackknife-mitigating-the-influence-of-biased-training-data-points-without-refitting\nhttps://research.ibm.com/publications/equi-tuning-group-equivariant-fine-tuning-of-pretrained-models\nhttps://research.ibm.com/publications/fairness-reprogramming\nhttps://research.ibm.com/topics/trustworthy-ai#tools\nhttps://newsroom.ibm.com/Whitepaper-A-Policymakers-Guide-to-Foundation-Models\nhttps://research.ibm.com/blog/generative-ai-for-enterprise\nhttps://newsroom.ibm.com/Whitepaper-A-Policymakers-Guide-to-Foundation-Models\n\n\n26 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nSupervis\u00e3o humana e an\u00e1lise humana no loop \nA supervis\u00e3o e revis\u00e3o humanas podem ajudar a identificar e corrigir \nerros e vieses no output gerado. Al\u00e9m disso, a valida\u00e7\u00e3o e o feedback \nhumanos sobre a qualidade das respostas do modelo ajudam a garantir \nque o conte\u00fado gerado seja preciso, relevante, de alta qualidade, \nn\u00e3o\u00a0esteja divergindo e esteja alinhado. \nLeia mais aqui \u2192\n\nCompromisso de consultoria \nA IBM Consulting se dedica a ajudar os clientes com o uso seguro  \ne respons\u00e1vel da IA, independentemente do stack tecnol\u00f3gico preferido. \nEles ajudam os clientes a cultivar uma cultura que adota e expande a \nIA com seguran\u00e7a, cria ferramentas de investiga\u00e7\u00e3o para ver dentro de \nalgoritmos de caixa preta e garante que a estrat\u00e9gia corporativa dos \nclientes inclua princ\u00edpios s\u00f3lidos de governan\u00e7a de dados. \nLeia mais aqui \u2192\n\nIBM Enterprise Design Thinking \nOs m\u00e9todos e estruturas IBM Enterprise Design Thinking, como o Team \nEssentials for AI, ajudam os clientes a definir comportamentos \u00e9ticos \nem\u00a0todo o processo de design e desenvolvimento de IA. \nLeia mais aqui \u2192\n\nRevis\u00e3o \u00e9tica da IA \nAvalia\u00e7\u00e3o de capacidades, limita\u00e7\u00f5es e riscos em projetos de IA ajudam \na garantir o desenvolvimento e uso respons\u00e1vel da tecnologia.\n\n\u00c9tica por Design \nA \u00c9tica por Design \u00e9 um framework estruturado com o objetivo de \nintegrar \u00e9tica tecnol\u00f3gica no pipeline de desenvolvimento de tecnologia, \nincluindo, entre outros, sistemas de IA. A \u00c9tica por Design viabiliza IA e \noutras tecnologias como uma for\u00e7a para o bem, incorporando princ\u00edpios \nde \u00e9tica tecnol\u00f3gica em produtos, servi\u00e7os e opera\u00e7\u00f5es mais amplas.\n\nDiversidade na equipe \nA diversidade nas equipes que desenvolvem e treinam sistemas de \nIA, incluindo modelos de base, ajuda a garantir que uma variedade \nde perspectivas e experi\u00eancias sejam consideradas. Essa diversidade \nmelhora a precis\u00e3o e o desempenho dos sistemas de IA e ajuda a \nreduzir os riscos ao longo do ciclo de vida de IA, incluindo o potencial \npara desfechos adversos que afetam grupos que podem n\u00e3o ser bem \nrepresentados em equipes menos diversificadas.\n\nhttps://research.ibm.com/blog/generative-ai-for-enterprise\nhttps://www.ibm.com/blog/announcement/ibm-consulting-unveils-center-of-excellence-for-generative-ai/\nhttps://www.ibm.com/design/thinking/page/badges/ai\n\n\n27 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nPol\u00edticas, regulamentos \ne melhores pr\u00e1ticas \nde\u00a0IA\n\nUm Guia dos Formuladores de Pol\u00edticas para Modelos de Base apresenta \no que os formuladores de pol\u00edticas precisam saber sobre modelos de \nbase. Este blog, do Laborat\u00f3rio de Pol\u00edticas da IBM, tem como objetivo \najudar os formuladores de pol\u00edticas na tarefa complexa de regular \no\u00a0uso de IA generativa, visando evitar os riscos sem limitar a inova\u00e7\u00e3o \ne as oportunidades ben\u00e9ficas. Para obter mais informa\u00e7\u00f5es sobre as \nrecomenda\u00e7\u00f5es da IBM aos formuladores de pol\u00edticas, leia o depoimento \nda Diretora de Privacidade e Confian\u00e7a da IBM, Christina Montgomery, \ndiante da Subcomiss\u00e3o Judici\u00e1ria de Privacidade, Tecnologia e Lei do \nSenado dos EUA aqui.\n\nA IBM est\u00e1 causando um impacto na forma\u00e7\u00e3o de pol\u00edticas regulat\u00f3rias, \nmelhores pr\u00e1ticas e ferramentas do setor, controle de tecnologias \nemergentes e pesquisa sociot\u00e9cnica, liderando e contribuindo \npara\u00a0iniciativas com organiza\u00e7\u00f5es como:\n\n \u2013 O F\u00f3rum Econ\u00f4mico Mundial\n \u2013 Parceria em IA\n \u2013 Centro de controle de IA da Associa\u00e7\u00e3o Internacional de Profissionais \n\nde Privacidade (IAPP)\n \u2013 Iniciativa global de IEEE sobre \u00e9tica de sistemas aut\u00f4nomos e \n\ninteligentes \n \u2013 Participa\u00e7\u00e3o de Christina Montgomery do National Artificial \n\nIntelligence Advisory Committee (NAIAC)\n \u2013 O Pacto Digital Global das Na\u00e7\u00f5es Unidas\n \u2013 A Parceria Global em Intelig\u00eancia Artificial (GPAI)\n \u2013 A Organiza\u00e7\u00e3o para Coopera\u00e7\u00e3o e Desenvolvimento Econ\u00f4mico \n\n(OECD)\n \u2013 A Data & Trust Alliance\n\nA IBM tem parcerias acad\u00eamicas s\u00f3lidas, como o MIT-IBM Watson \nAI\u00a0Lab, onde uma comunidade de cientistas do MIT e da IBM Research \nconduzem pesquisas sobre IA e trabalham com organiza\u00e7\u00f5es globais \npara unir algoritmos ao seu impacto nos neg\u00f3cios e na sociedade. \nO\u00a0Notre Dame-IBM Tech Ethics Lab foi formado para abordar as diversas \nquest\u00f5es \u00e9ticas implicadas pelo desenvolvimento e uso de tecnologias \navan\u00e7adas, incluindo IA, aprendizado de m\u00e1quina (ML) e computa\u00e7\u00e3o \nqu\u00e2ntica. A pesquisa de Intelig\u00eancia Artificial Centrada no Homem (HAI) \nda Universidade de Stanford promove pesquisas, educa\u00e7\u00e3o, pol\u00edticas \ne\u00a0pr\u00e1ticas de IA.\n\nhttps://newsroom.ibm.com/Whitepaper-A-Policymakers-Guide-to-Foundation-Models\nhttps://www.judiciary.senate.gov/imo/media/doc/2023-05-16 - Testimony - Montgomery.pdf\n\n\n28 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\nContinue acompanhando este espa\u00e7o \npara obter mais informa\u00e7\u00f5es sobre os \n\u00faltimos avan\u00e7os em modelos de base \ne como a IBM est\u00e1 trabalhando para o \ndesenvolvimento respons\u00e1vel e uso desta \ne de outras tecnologias.\n\n\n\n\u00a9 Copyright IBM Corporation 2023, 2024\n\nIBM Brasil Ltda \nRua Tut\u00f3ia, 1157 \nCEP 04007-900 \nS\u00e3o Paulo, SP \nIBM Corporation \nNew Orchard Road \nArmonk, NY 10504 \n \nProduzido nos  \nEstados Unidos da Am\u00e9rica \nFevereiro de 2024\n\nIBM, o logotipo da IBM, Enterprise Design Thinking, IBM Consulting, IBM Research,  \nIBM Watson, watsonx, watsonx.ai, watsonx.data e watsonx.governance s\u00e3o marcas \ncomerciais ou marcas registradas da International Business Machines Corporation, \nnos Estados Unidos e/ou em outros pa\u00edses. Outros nomes de produtos e servi\u00e7os  \npodem ser marcas comerciais da IBM ou de outras empresas. Uma lista atual de \nmarcas comerciais da IBM est\u00e1 dispon\u00edvel em ibm.com/br-pt/trademark.\n\nEste documento \u00e9 atual na data de sua publica\u00e7\u00e3o inicial, podendo ser alterado \npela IBM a qualquer momento. Nem todas as ofertas est\u00e3o dispon\u00edveis em todos os \npa\u00edses nos quais a IBM opera. \n\nAS INFORMA\u00c7\u00d5ES CONTIDAS NESTE DOCUMENTO S\u00c3O FORNECIDAS NO ESTADO \nEM QUE SEM ENCONTRAM, SEM QUALQUER GARANTIA, EXPRESSA OU IMPL\u00cdCITA, \nINCLUSIVE SEM QUALQUER GARANTIA DE COMERCIALIZA\u00c7\u00c3O, ADEQUA\u00c7\u00c3O A \nDETERMINADO FIM E QUALQUER GARANTIA OU CONDI\u00c7\u00c3O DE N\u00c3O INFRA\u00c7\u00c3O. \nOs produtos IBM t\u00eam a garantia prevista nos termos e condi\u00e7\u00f5es dos contratos sob \nos quais s\u00e3o fornecidos.\n\nDeclara\u00e7\u00e3o de boas pr\u00e1ticas de seguran\u00e7a: nenhum sistema ou produto de TI deve \nser considerado completamente seguro, e nenhuma medida exclusiva de produto, \nservi\u00e7o ou seguran\u00e7a pode ser completamente eficaz na preven\u00e7\u00e3o de uso ou \nacesso inadequado. A IBM n\u00e3o garante que nenhum de seus sistemas, produtos \nou servi\u00e7os estejam imunes nem que tornar\u00e3o sua empresa imune a condutas \nmaliciosas ou ilegais por parte de terceiros. \n\nO cliente \u00e9 respons\u00e1vel por garantir o cumprimento de todas as leis e regulamentos \naplic\u00e1veis. A IBM n\u00e3o fornece conselho jur\u00eddico tampouco representa ou garante \nque seus servi\u00e7os ou produtos garantir\u00e3o que o cliente esteja em conformidade \ncom qualquer lei ou regulamenta\u00e7\u00e3o. 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Suas principais preocupa\u00e7\u00f5es s\u00e3o \r\nciberseguran\u00e7a (57%), privacidade (51%) e precis\u00e3o (47%). Muitas \r\norganiza\u00e7\u00f5es estavam levando essas preocupa\u00e7\u00f5es a s\u00e9rio antes \r\nda \u2018consumeriza\u00e7\u00e3o\u2019 da IA generativa, expressando sua inten\u00e7\u00e3o de \r\ninvestir pelo menos 40% mais em \u00e9tica de IA nos pr\u00f3ximos tr\u00eas anos. \r\nA\u00a0conscientiza\u00e7\u00e3o sobre riscos e poss\u00edveis maneiras de mitig\u00e1-los \u00e9 o \r\nprimeiro passo crucial para a cria\u00e7\u00e3o de sistemas de IA confi\u00e1veis.\r\nNeste documento:\r\nExploraremos as vantagens dos modelos de base, incluindo \r\nsua capacidade de realizar tarefas desafiadoras, potencial \r\npara acelerar a ado\u00e7\u00e3o de IA, habilidade de aumentar \r\na produtividade e os benef\u00edcios econ\u00f4micos que eles \r\nproporcionam.\r\nDiscutiremos as tr\u00eas categorias de risco, incluindo riscos \r\nconhecidos de formas anteriores de IA, riscos conhecidos \r\namplificados por modelos de base e riscos emergentes \r\nintr\u00ednsecos aos recursos generativos dos modelos de base.\r\nAbordaremos os princ\u00edpios, os pilares e o controle que \r\nformam a base das iniciativas \u00e9ticas de IA da IBM e \r\nsugeriremos barreiras para a mitiga\u00e7\u00e3o de riscos.5 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nIntrodu\u00e7\u00e3o\r\n\u00c0 medida que o uso de IA continua se expandindo, os grandes e \r\ncomplexos modelos de IA est\u00e3o fornecendo resultados promissores \r\nde desempenho, bem como resolvendo alguns dos problemas mais \r\ndesafiadores da sociedade. No entanto, criar grandes conjuntos de dados \r\nde treinamento e modelos complexos para cada aplicativo de IA pode \r\nser extremamente dif\u00edcil para as empresas. Modelos de base fornecem \r\num caminho para alcan\u00e7ar o melhor dos dois mundos: desenvolver \r\nmodelos de \u00faltima gera\u00e7\u00e3o poderosos e reutiliz\u00e1-los diretamente ou \r\naplicar m\u00e9todos de ajuste para implementar uma variedade de casos \r\nde uso, em vez de treinar novos modelos para cada caso de uso. Por \r\nexemplo, a IBM Research desenvolveu modelos de base para inspe\u00e7\u00e3o \r\nvisual. Esses modelos de base aprendem a representa\u00e7\u00e3o geral de \r\nsuperf\u00edcies e corredores de concreto e podem ser ajustados ainda \r\nmais para casos de uso espec\u00edficos, como detec\u00e7\u00e3o de rachaduras ou \r\ninspe\u00e7\u00e3o de defeitos com dados menos rotulados.\r\nA IBM define um modelo de base como um modelo de IA que pode \r\nser adaptado a uma ampla gama de tarefas de recebimento de dados. \r\nOs\u00a0modelos de base normalmente s\u00e3o modelos generativos de grande \r\nescala treinados em dados n\u00e3o rotulados usando autossupervis\u00e3o. \r\nComo\u00a0modelos de grande escala, os modelos de base podem incluir \r\nbilh\u00f5es de par\u00e2metros.\r\nA IBM \u00e9 uma empresa de nuvem h\u00edbrida e IA com vasta reputa\u00e7\u00e3o como \r\nadministradora de dados respons\u00e1vel e comprometida com a \u00e9tica em \r\nIA. Usando a capacidade de nossas equipes de pesquisa, produto e \r\nconsultoria, juntamente com parceiros externos, como a Hugging Face, \r\najudamos a trazer o poder dos modelos de base para nossos clientes \r\ne a criar IAs confi\u00e1veis em qualquer empresa. A IBM tamb\u00e9m continua \r\ninvestindo na cria\u00e7\u00e3o de novas plataformas, como a IA IBM watsonx\r\ne plataformas e tecnologias de dados, para projetar e desenvolver \r\nmodelos de IA para se comportar de maneira audit\u00e1vel e confi\u00e1vel. \r\nEste documento descreve o ponto de vista da IBM sobre a \u00e9tica dos \r\nmodelos de base. \u00c9 a primeira vers\u00e3o, e as vers\u00f5es futuras expandir\u00e3o \r\nv\u00e1rios aspectos da abordagem \u00e9tica do modelo de base da IBM. \r\nEsperamos que este documento seja \u00fatil para todos os stakeholders no \r\ndesenvolvimento, implementa\u00e7\u00e3o e uso do modelo de base de forma \r\nrespons\u00e1vel.6 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nBenef\u00edcios dos \r\nmodelos de base\r\nOs modelos de base podem melhorar significativamente o processo de \r\ndesenvolvimento de sistemas de IA e auxiliar no avan\u00e7o da IA da fase de \r\nexplora\u00e7\u00e3o para a ado\u00e7\u00e3o nas empresas. Seus benef\u00edcios incluem:\r\nRealizar tarefas complexas\r\nModelos de base mostram um aumento significativo no desempenho \r\nna resolu\u00e7\u00e3o de problemas complexos e dif\u00edceis. Por exemplo, \r\no modelo de base geoespacial da colabora\u00e7\u00e3o IBM e NASA foi \r\nprojetado para converter os dados de sat\u00e9lite da NASA em mapas \r\nde desastres naturais, como inunda\u00e7\u00f5es e outras mudan\u00e7as de \r\ncen\u00e1rio. O modelo tamb\u00e9m pode ser usado para ajudar a revelar \r\no\u00a0passado do nosso planeta; estimar riscos para culturas, empresas \r\nou infraestruturas devido ao clima severo; desenvolver estrat\u00e9gias \r\npara se adaptar \u00e0s mudan\u00e7as clim\u00e1ticas; e auxiliar no agroneg\u00f3cio. \r\nO modelo est\u00e1 planejado para ser disponibilizado previamente aos \r\nclientes IBM por meio do IBM Environmental Intelligence Suite.\r\nPara ilustrar, o MoLFormer-XL da IBM \u00e9 um modelo de base que \r\n\u00e9\u00a0capaz de inferir a estrutura de mol\u00e9culas a partir de representa\u00e7\u00f5es \r\nsimples, tornando mais f\u00e1cil a aprendizagem de v\u00e1rias tarefas \r\nde recebimento de dados, como prever as propriedades f\u00edsicas \r\ne\u00a0qu\u00e2nticas de uma mol\u00e9cula, identificar mol\u00e9culas semelhantes, \r\nrastrear mol\u00e9culas j\u00e1 aprovadas para novos casos de uso e descobrir \r\nnovas mol\u00e9culas. Moderna e IBM est\u00e3o explorando formas de \r\nusar o MoLExer para ajudar a prever propriedades das mol\u00e9culas e \r\nentender as caracter\u00edsticas de poss\u00edveis medicamentos de mRNA.\r\nMaior produtividade\r\nA natureza generativa dos modelos de base amplia o n\u00famero de \r\n\u00e1reas em que a IA pode ser usada em uma empresa para ajudar a \r\nmelhorar a\u00a0produtividade, automatizando tarefas rotineiras e tediosas \r\ne\u00a0permitindo que os usu\u00e1rios dediquem mais tempo ao trabalho \r\ncriativo e inovador. Por exemplo, o IBM Watsonx Code Assistant, \r\ndesenvolvido com modelos de base, possibilita que desenvolvedores, \r\nindependentemente do n\u00edvel de experi\u00eancia, escrevam c\u00f3digos usando \r\nrecomenda\u00e7\u00f5es geradas por IA.\r\nTime to value mais r\u00e1pido\r\nModelos de base geralmente s\u00e3o treinados com dados n\u00e3o rotulados, \r\nque est\u00e3o mais dispon\u00edveis em grandes quantidades do que dados \r\nrotulados. Uma vez treinados, os modelos de base podem ser usados \r\ndiretamente ou ap\u00f3s serem ajustados para aplicativos de recebimento \r\nde dados, usando uma pequena quantidade de dados rotulados \r\nespecializados, que podem diminuir a cria\u00e7\u00e3o do time to value.7 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nUtilize diversas modalidades de dados\r\nOs modelos de base podem ser treinados usando diversas modalidades \r\nde dados, como l\u00edngua natural, texto, imagem e \u00e1udio. Eles tamb\u00e9m \r\npodem ser aplicados a tarefas que exigem diferentes tipos de dados, \r\ncomo dados de s\u00e9ries temporais, dados geoespaciais, dados tabulares, \r\ndados semiestruturados e dados de modalidade mista, como texto \r\ncombinado com imagens.\r\nDespesas amortizadas\r\nEmbora o custo inicial do treinamento de um modelo de base seja \r\nsignificativamente maior do que o treinamento de um modelo de IA \r\ntradicional, o custo adicional para aplic\u00e1-lo em uma nova tarefa \u00e9 \r\nconsideravelmente menor. O uso de modelos de base pr\u00e9-treinados \r\npoderia ajudar a eliminar a necessidade de que as empresas fa\u00e7am \r\ninvestimentos substanciais para treinar modelos de base e explorar suas \r\nnovas capacidades. Para uma empresa, a confiabilidade dos modelos, \r\na\u00a0efici\u00eancia energ\u00e9tica, o desempenho, a portabilidade e a capacidade \r\nde\u00a0usar dados corporativos de forma eficaz e segura s\u00e3o fundamentais.\r\nA IBM permite que as empresas \r\ncriem e detenham o valor de \r\nmodelos de base para seus \r\nneg\u00f3cios, trazendo as melhores \r\ninova\u00e7\u00f5es da comunidade de \r\nIA aberta e global, operando de \r\nforma eficiente em ambientes de \r\ncomputa\u00e7\u00e3o h\u00edbrida, ajudando \r\na mitigar riscos e controlando \r\nrigorosamente a IA.8 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nRiscos dos \r\nmodelos de base\r\nComo todas as tecnologias que avan\u00e7am rapidamente, os modelos \r\nde base oferecem riscos e benef\u00edcios. Alguns s\u00e3o riscos legais, \r\ncomo restri\u00e7\u00f5es \u00e0 movimenta\u00e7\u00e3o ou uso de dados, e precisam \r\nser cuidadosamente avaliados de acordo com a legisla\u00e7\u00e3o atual e \r\nem evolu\u00e7\u00e3o. Outros riscos t\u00eam uma natureza \u00e9tica e devem ser \r\nconsiderados cuidadosamente para que a tecnologia tenha um impacto \r\npositivo. Em geral, os riscos de IA levantam quest\u00f5es sociot\u00e9cnicas \r\ne\u00a0devem ser abordados e mitigados por meio de m\u00e9todos sociot\u00e9cnicos, \r\nincluindo ferramentas de software, processos de avalia\u00e7\u00e3o de risco, \r\nframeworks de \u00e9tica em IA, mecanismos de controle, consultas \r\nmultistakeholder, padr\u00f5es e regulamenta\u00e7\u00e3o. Iremos listar os riscos \r\nconsiderando as seguintes 3 categorias:\r\n1. Tradicional. Riscos conhecidos de formas anteriores ou anteriores \r\nde\u00a0sistemas de IA\r\n2. Amplificados. Riscos conhecidos, mas agora intensificados devido \u00e0s \r\ncaracter\u00edsticas intr\u00ednsecas dos modelos de base, principalmente seus \r\nrecursos generativos inerentes\r\n3. Novo. Riscos emergentes intr\u00ednsecos aos modelos de base e suas \r\ncapacidades generativas inerentes\r\nTamb\u00e9m estruturamos a lista de riscos em rela\u00e7\u00e3o a se est\u00e3o \r\nprincipalmente associados ao conte\u00fado fornecido ao modelo \r\nbase, o\u00a0input, ou ao conte\u00fado gerado por ele, o output, ou se est\u00e3o \r\nrelacionados a desafios adicionais.9 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\n1. Riscos associados \u00e0 entrada\r\nGrupo Risco Indicador\r\nFase de treinamento e ajuste\r\nJusti\u00e7a Vi\u00e9s de dados: vi\u00e9s hist\u00f3rico, representacional \r\ne social presente nos dados usados para \r\ntreinar e fazer o ajuste fino do modelo.\r\nTreinar um sistema de IA com dados enviesados, como vi\u00e9s hist\u00f3rico ou Amplificado\r\nrepresentacional, pode resultar em outputs enviesados ou distorcidos \r\nque podem representar injustamente ou discriminar certos grupos \r\nou indiv\u00edduos. Al\u00e9m dos impactos negativos na sociedade, entidades \r\ncomerciais podem enfrentar consequ\u00eancias legais, interrup\u00e7\u00e3o \r\ndas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o decorrentes dos resultados \r\nenviesados do modelo.\r\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\r\nEnvenenamento de dados: um tipo de ataque \r\nadversarial no qual um advers\u00e1rio ou agente \r\ninterno malicioso injeta intencionalmente \r\namostras corrompidas, falsas, enganosas \r\nou incorretas no conjunto de dados de \r\ntreinamento ou ajuste fino.\r\nO envenenamento de dados pode tornar o modelo sens\u00edvel a um padr\u00e3o Tradicional\r\nde dados malicioso e produzir o output desejado pelo advers\u00e1rio. Isso \r\npode criar um risco de seguran\u00e7a onde advers\u00e1rios podem manipular \r\no\u00a0comportamento do modelo em seu pr\u00f3prio benef\u00edcio. Al\u00e9m de produzir \r\nresultados n\u00e3o intencionais e potencialmente maliciosos, uma diverg\u00eancia \r\ndo modelo causada por envenenamento de dados pode resultar em \r\nentidades comerciais enfrentando consequ\u00eancias legais, interrup\u00e7\u00e3o \r\ndas\u00a0opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\r\nRobustez\r\nCuradoria de dados: quando os dados de \r\ntreinamento ou ajuste s\u00e3o coletados ou \r\npreparados de forma inadequada.\r\nUma curadoria de dados inadequada pode afetar adversamente como Amplificado\r\num modelo \u00e9 treinado, resultando em um modelo que n\u00e3o se comporta \r\nde acordo com os valores pretendidos. Exemplos de uma curadoria de \r\ndados inadequada podem incluir erros de rotulagem ou anota\u00e7\u00e3o nos \r\ndados usados para treinar ou ajustar o modelo. Corrigir problemas ap\u00f3s \r\no treinamento e a implementa\u00e7\u00e3o do modelo pode ser insuficiente para \r\ngarantir um comportamento adequado. Um comportamento inadequado do \r\nmodelo pode resultar em entidades comerciais enfrentando consequ\u00eancias \r\nlegais, interrup\u00e7\u00f5es nas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\r\nAlinhamento \r\nde valor\r\nRetreinamento baseado em downstream: \r\nusando de outputs indesej\u00e1veis (imprecisos, \r\ninadequados, conte\u00fado do usu\u00e1rio, etc.) \r\nde aplica\u00e7\u00f5es downstream para fins de \r\nretreinamento.\r\nO reaproveitamento de output downstream para treinar novamente um Novo\r\nmodelo sem implementar a verifica\u00e7\u00e3o humana adequada aumenta as \r\nchances de que outputs indesej\u00e1veis sejam incorporados aos dados de \r\ntreinamento ou ajuste do modelo, possivelmente gerando outputs ainda \r\nmais indesej\u00e1veis. Comportamento inadequado do modelo pode resultar \r\nem entidades empresariais enfrentando consequ\u00eancias legais ou danos \r\n\u00e0 reputa\u00e7\u00e3o. N\u00e3o cumprir com as leis de transfer\u00eancia de dados pode \r\nresultar em multas e outras consequ\u00eancias legais.\r\nTransfer\u00eancia de dados: leis e outras \r\nrestri\u00e7\u00f5es podem limitar ou proibir a \r\ntransfer\u00eancia de dados.\r\nRestri\u00e7\u00f5es \u00e0 transfer\u00eancia de dados podem afetar a disponibilidade dos Tradicional\r\ndados necess\u00e1rios para treinar um modelo de IA e podem resultar em \r\ndados mal representados. Al\u00e9m do impacto na disponibilidade de dados, \r\no n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de transfer\u00eancia de dados \r\npode resultar em multas e outras consequ\u00eancias legais. \r\nLeis de dados\r\nUso de dados: leis e outras restri\u00e7\u00f5es podem \r\nlimitar ou proibir o uso de alguns dados para \r\ncasos de uso espec\u00edficos de IA.\r\nO n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de uso de dados pode Tradicional\r\nresultar em multas e outras consequ\u00eancias legais. \r\nAquisi\u00e7\u00e3o de dados: leis e outras \r\nregulamenta\u00e7\u00f5es podem limitar a coleta \r\nde certos tipos de dados para casos de uso \r\nespec\u00edficos de IA.\r\nO n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es da aquisi\u00e7\u00e3o de dados Amplificado\r\npode resultar em multas e outras consequ\u00eancias legais. 10 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nGrupo Risco Indicador\r\nPropriedade \r\nintelectual\r\nDireitos de uso de dados: termos de servi\u00e7o, \r\nleis de direitos autorais, conformidade com \r\nlicen\u00e7as ou outras quest\u00f5es de propriedade \r\nintelectual podem restringir a capacidade \r\nde usar certos dados para a constru\u00e7\u00e3o \r\nde\u00a0modelos. \r\nAs leis e regulamenta\u00e7\u00f5es referentes ao uso de dados para treinar IA Amplificado\r\ns\u00e3o inst\u00e1veis e podem variar de pa\u00eds para pa\u00eds, o que cria desafios no \r\ndesenvolvimento de modelos. Se o uso de dados violar regras ou restri\u00e7\u00f5es, \r\nas entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \r\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\r\nTranspar\u00eancia de dados: desafio em \r\ndocumentar como os dados de um modelo \r\nforam coletados, curados e utilizados \r\npara\u00a0trein\u00e1-lo.\r\nA transpar\u00eancia dos dados \u00e9 importante para a conformidade legal e \u00e9tica Amplificado\r\nda IA. A falta de informa\u00e7\u00f5es limita a capacidade de avaliar os riscos \r\nassociados aos dados. A falta de requisitos padronizados pode limitar a \r\ndivulga\u00e7\u00e3o, pois as organiza\u00e7\u00f5es protegem segredos comerciais e tentam \r\nevitar que outros copiem seus modelos.\r\nTranspar\u00eancia\r\nProced\u00eancia dos dados: desafio em \r\npadronizar e estabelecer m\u00e9todos para \r\nverificar de onde os dados vieram.\r\nNem todas as fontes de dados s\u00e3o confi\u00e1veis. Os dados podem ter sido Amplificado\r\ncoletados, manipulados ou falsificados de forma anti\u00e9tica. O uso de dados \r\nn\u00e3o confi\u00e1veis pode resultar em comportamentos indesej\u00e1veis no modelo. \r\nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \r\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nInforma\u00e7\u00f5es pessoais nos dados: inclus\u00e3o \r\nou presen\u00e7a de informa\u00e7\u00f5es pessoalmente \r\nidentific\u00e1veis (PII) e informa\u00e7\u00f5es pessoais \r\nsens\u00edveis (SPI) nos dados usados para treinar \r\nou ajustar o modelo.\r\nSe n\u00e3o desenvolvido adequadamente para proteger dados sens\u00edveis, Tradicional\r\no\u00a0modelo pode expor informa\u00e7\u00f5es pessoais no output gerado. Al\u00e9m disso, \r\ndados pessoais ou sens\u00edveis devem ser revisados e tratados de acordo \r\ncom as leis e regulamenta\u00e7\u00f5es de privacidade. As entidades empresariais \r\npodem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es \r\ne\u00a0outras consequ\u00eancias legais se forem encontradas em viola\u00e7\u00e3o.\r\nPrivacidade\r\nReidentifica\u00e7\u00e3o: mesmo com a remo\u00e7\u00e3o de \r\ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis \r\n(PII) e informa\u00e7\u00f5es pessoais sens\u00edveis (SPI) \r\ndos dados, ainda pode ser poss\u00edvel identificar \r\npessoas devido a outros recursos dispon\u00edveis \r\nnos dados. \r\nOs dados que podem revelar informa\u00e7\u00f5es pessoais ou sens\u00edveis devem Tradicional\r\nser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es de privacidade, pois \r\nas entidades comerciais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \r\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se forem \r\nconsideradas em viola\u00e7\u00e3o.\r\nDireitos de privacidade de dados: desafios \r\nrelacionados \u00e0 capacidade de fornecer \r\ndireitos do titular dos dados, como op\u00e7\u00e3o \r\nde exclus\u00e3o, direito de acesso e direito ao \r\nesquecimento.\r\nA identifica\u00e7\u00e3o ou uso inadequado de dados pode resultar em viola\u00e7\u00e3o das Amplificado\r\nleis de privacidade. O uso inadequado ou um pedido de remo\u00e7\u00e3o de dados \r\npoderia obrigar as organiza\u00e7\u00f5es a reconfigurar o modelo, o que \u00e9 caro. \r\nAl\u00e9m disso, as entidades empresariais podem enfrentar multas, danos \u00e0 \r\nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se n\u00e3o \r\ncumprirem as regras e regulamenta\u00e7\u00f5es de privacidade de dados.\r\nConsentimento informado: dados \r\ncoletados para treinar modelos de IA sem \r\no consentimento informado do propriet\u00e1rio, \r\nmesmo quando legalmente permitido.\r\nEm algumas circunst\u00e2ncias, pode ser anti\u00e9tico coletar e usar dados Tradicional\r\nsem o\u00a0consentimento da pessoa. Existem tamb\u00e9m poss\u00edveis riscos \r\nreputacionais associados a esse tipo de uso.11 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nGrupo Risco Indicador\r\nInfer\u00eancia Fase\r\nPrivacidade Informa\u00e7\u00f5es pessoais no prompt: divulgar \r\ninforma\u00e7\u00f5es pessoais ou informa\u00e7\u00f5es \r\npessoais sens\u00edveis como parte do prompt \r\nsolicita\u00e7\u00e3o enviada ao modelo.\r\nOs dados do prompt podem ser armazenados ou posteriormente utilizados Novo\r\npara outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos \r\nde dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es \r\nde privacidade. Sem um armazenamento e uso adequados dos dados, \r\nas entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \r\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\r\nInforma\u00e7\u00f5es de IP no prompt: divulga\u00e7\u00e3o de \r\ninforma\u00e7\u00f5es de direitos autorais ou outras \r\ninforma\u00e7\u00f5es de propriedade intelectual como \r\nparte do prompt enviado ao modelo.\r\nOs dados do prompt podem ser armazenados ou posteriormente utilizados Novo\r\npara outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos \r\nde dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es \r\nde propriedade intelectual. Sem um armazenamento e uso adequados \r\ndos dados, as entidades empresariais podem enfrentar multas, danos \r\n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nPropriedade \r\nintelectual\r\nDados confidenciais no prompt: inclus\u00e3o de \r\ndados confidenciais como parte do prompt \r\nenviado ao modelo.\r\nSe n\u00e3o for desenvolvido adequadamente para proteger dados confidenciais, Novo\r\no modelo pode expor informa\u00e7\u00f5es confidenciais ou propriedade intelectual \r\nno output gerado. Al\u00e9m disso, informa\u00e7\u00f5es confidenciais dos usu\u00e1rios finais \r\npodem ser coletadas e armazenadas inadvertidamente.\r\nRobustez Ataque de evas\u00e3o: tentativa de fazer com \r\nque um modelo produza outputs incorretos \r\nperturbando os dados enviados ao modelo \r\ntreinado.\r\nOs ataques de evas\u00e3o alteram o comportamento do modelo, geralmente Amplificado\r\npara beneficiar o atacante. Se os resultados de output n\u00e3o forem \r\ndevidamente considerados, as entidades empresariais podem enfrentar \r\nmultas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \r\nconsequ\u00eancias legais.\r\nAtaques baseados em prompt: ataques \r\nadversos, como inje\u00e7\u00e3o de prompt (tentativa \r\nde for\u00e7ar um modelo a produzir um output \r\ninesperado), vazamento de prompt (tentativas \r\nde extrair o prompt do sistema de um \r\nmodelo), desbloqueio (tentativas de romper \r\nas prote\u00e7\u00f5es estabelecidas no modelo), \r\ne\u00a0prepara\u00e7\u00e3o de prompt (tentativa de for\u00e7ar \r\num modelo a produzir um output alinhado \r\nao\u00a0prompt).\r\nDependendo do conte\u00fado revelado, as entidades empresariais podem Novo\r\nenfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \r\nconsequ\u00eancias legais.12 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\n2. Riscos associados \u00e0 sa\u00edda\r\nGrupo Risco Indicador\r\nJusti\u00e7a Vi\u00e9s de output: o conte\u00fado gerado pode \r\nrepresentar injustamente certos grupos ou \r\nindiv\u00edduos.\r\nO vi\u00e9s pode prejudicar os usu\u00e1rios dos modelos de IA e amplificar Novo\r\ncomportamentos discriminat\u00f3rios existentes. As entidades empresariais \r\npodem enfrentar danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \r\nconsequ\u00eancias.\r\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\r\nVi\u00e9s de decis\u00e3o: quando um grupo \u00e9 \r\ninjustamente favorecido em rela\u00e7\u00e3o a outro \r\ndevido aos efeitos das decis\u00f5es tomadas por \r\nhumanos usando o output do modelo.\r\nO vi\u00e9s pode prejudicar as pessoas afetadas pelas decis\u00f5es do modelo. Tradicional\r\nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \r\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nViola\u00e7\u00e3o de direitos autorais: quando \r\num modelo gera conte\u00fado que \u00e9 muito \r\nsemelhante ou id\u00eantico a uma obra existente \r\nprotegida por direitos autorais ou abrangida \r\npor um acordo de licen\u00e7a de c\u00f3digo aberto.\r\nAs leis e regulamenta\u00e7\u00f5es referentes ao uso de conte\u00fado que se assemelha Novo\r\nou \u00e9 muito semelhante a outros dados protegidos por direitos autorais s\u00e3o \r\namplamente indefinidos e podem variar de pa\u00eds para pa\u00eds, o que representa \r\ndesafios na determina\u00e7\u00e3o e implementa\u00e7\u00e3o da conformidade. As entidades \r\nempresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das \r\nopera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nPropriedade \r\nintelectual\r\nAlucina\u00e7\u00e3o: gera\u00e7\u00e3o de conte\u00fado \r\nfactualmente impreciso ou n\u00e3o verdadeiro.\r\nOutputs falsos podem induzir os usu\u00e1rios ao erro e serem incorporados Novo\r\nem artefatos posteriores, propagando ainda mais a desinforma\u00e7\u00e3o. Isso \r\npode prejudicar tanto os propriet\u00e1rios quanto os usu\u00e1rios dos modelos de \r\nIA. Tamb\u00e9m, as entidades empresariais podem enfrentar multas, danos \r\n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nOutputs t\u00f3xicos: quando o modelo produz \r\nconte\u00fado odioso, abusivo e profano (HAP) ou \r\nobsceno.\r\nConte\u00fado odioso, abusivo e profano (HAP) ou obsceno pode impactar Novo\r\nadversamente e prejudicar as pessoas que interagem com o modelo. \r\nTamb\u00e9m, as entidades empresariais podem enfrentar multas, danos \u00e0 \r\nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nAlinhamento de \r\nvalor\r\nConselhos perigosos: quando um modelo \r\nfornece conselhos sem ter informa\u00e7\u00f5es \r\nsuficientes, resultando em poss\u00edveis perigos \r\nse o conselho for seguido.\r\nUma pessoa pode agir com base em conselhos incompletos ou Novo\r\npreocupar-se com uma situa\u00e7\u00e3o que n\u00e3o se aplica a ela devido \u00e0 natureza \r\nsupergeneralizada do conte\u00fado gerado.\r\nDissemina\u00e7\u00e3o de desinforma\u00e7\u00e3o: utiliza\u00e7\u00e3o \r\nde um modelo para criar informa\u00e7\u00f5es \r\nenganosas ou falsas com o intuito de enganar \r\nou influenciar um p\u00fablico-alvo.\r\nEspalhar desinforma\u00e7\u00e3o pode afetar a capacidade de uma pessoa Novo\r\nde tomar decis\u00f5es informadas. As entidades empresariais podem \r\nenfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es \r\ne\u00a0outras consequ\u00eancias\u00a0legais.\r\nToxicidade: utilizar um modelo para gerar \r\nconte\u00fado odioso, abusivo e profano (HAP) \r\nou\u00a0obsceno.\r\nConte\u00fado t\u00f3xico pode ter um impacto negativo no bem-estar de seus Novo\r\ndestinat\u00e1rios. As entidades empresariais podem enfrentar multas, danos \r\n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nUso indevido \r\nUso n\u00e3o consensual: utilizar um modelo para \r\nimitar pessoas por meio de v\u00eddeo (deepfakes), \r\nimagens, \u00e1udio ou outras modalidades sem \r\no\u00a0consentimento delas.\r\nDeepfakes podem disseminar desinforma\u00e7\u00e3o sobre uma pessoa, Amplificado\r\npossivelmente resultando em impactos negativos na reputa\u00e7\u00e3o da pessoa. \r\nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \r\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.Expor informa\u00e7\u00f5es pessoais: quando \r\ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis (PII) \r\nou informa\u00e7\u00f5es pessoais sens\u00edveis (SPI) s\u00e3o \r\nutilizadas nos dados de treinamento, dados \r\nde ajuste fino ou como parte do prompt, \r\nos modelos podem revelar esses dados no \r\noutput gerado.\r\nCompartilhar informa\u00e7\u00f5es pessoalmente identific\u00e1veis das pessoas afeta Novo\r\nseus direitos e as torna mais vulner\u00e1veis. Al\u00e9m disso, os dados dos outputs \r\ndevem ser revisados em conformidade com as leis e regulamenta\u00e7\u00f5es de \r\nprivacidade, pois as entidades comerciais podem enfrentar multas, danos \r\n\u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se \r\nforem encontradas em viola\u00e7\u00e3o das leis ou regulamenta\u00e7\u00f5es de\u00a0privacidade \r\nou uso de dados. \r\nPrivacidade\r\nOutput inexplic\u00e1vel: desafios em explicar por \r\nque o output do modelo foi gerado.\r\nOs modelos de base s\u00e3o baseados em arquiteturas complexas de Amplificado \r\ndeep learning, tornando as explica\u00e7\u00f5es para seus outputs dif\u00edceis. \r\nSem\u00a0explica\u00e7\u00f5es claras para o output do modelo, \u00e9 dif\u00edcil para os usu\u00e1rios, \r\nvalidadores do modelo e auditores entenderem e confiarem no modelo. \r\nA\u00a0falta de transpar\u00eancia pode acarretar consequ\u00eancias legais em dom\u00ednios \r\naltamente regulamentados. Explica\u00e7\u00f5es equivocadas podem levar a uma \r\nconfian\u00e7a excessiva.\r\nExplicabilidade\r\nAtribui\u00e7\u00e3o n\u00e3o confi\u00e1vel de fontes: \r\ndesafios em determinar de quais dados de \r\ntreinamento ou ajuste fino o modelo gerou \r\numa parte ou todo o seu output.\r\nA incapacidade de rastrear a origem ou proced\u00eancia da sa\u00edda torna Novo\r\ndif\u00edcil para os usu\u00e1rios, validadores de modelo e auditores entenderem \r\ne\u00a0confiarem no modelo.\r\nRastreabilidade\r\nExcesso/falta de confian\u00e7a: quando uma \r\npessoa deposita confian\u00e7a em excesso ou em \r\nfalta na orienta\u00e7\u00e3o de um modelo de IA.\r\nEm tarefas onde os humanos baseiam suas escolhas em sugest\u00f5es da IA, Amplificado\r\numa confian\u00e7a excessiva ou insuficiente pode levar a decis\u00f5es inadequadas \r\ndevido \u00e0 confian\u00e7a equivocada no sistema de IA, com consequ\u00eancias \r\nnegativas que aumentam com a import\u00e2ncia da decis\u00e3o. Decis\u00f5es ruins \r\npodem prejudicar as pessoas e podem resultar em preju\u00edzos financeiros, \r\ndanos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias \r\nlegais para as entidades comerciais.\r\nConfian\u00e7a \r\nequivocada\r\n13 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nGrupo Risco Por que isso \u00e9 uma preocupa\u00e7\u00e3o? Indicador\r\nUso perigoso: utilizar um modelo com a \u00fanica \r\ninten\u00e7\u00e3o de prejudicar pessoas.\r\nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, Novo\r\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nUso inadequado: utilizar um modelo para um \r\nfim para o qual o modelo n\u00e3o foi projetado.\r\nReutilizar um modelo sem compreender seus dados originais, inten\u00e7\u00e3o Amplificado\r\nde design e objetivos pode resultar em comportamentos inesperados \r\ne\u00a0indesejados do modelo.\r\nGera\u00e7\u00e3o de c\u00f3digo prejudicial: modelos \r\npodem gerar c\u00f3digo que, quando executado, \r\ncausa danos ou afeta inadvertidamente \r\noutros sistemas.\r\nA execu\u00e7\u00e3o de c\u00f3digo prejudicial pode abrir vulnerabilidades nos sistemas Novo\r\nde TI. As entidades empresariais podem enfrentar multas, danos \u00e0 \r\nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nGera\u00e7\u00e3o \r\nde c\u00f3digo \r\nprejudicial\r\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o revelar que o conte\u00fado \r\n\u00e9\u00a0gerado por um modelo de IA.\r\nA omiss\u00e3o do conte\u00fado produzido por IA pode ser interpretada como Novo\r\nenganosa, levando a uma diminui\u00e7\u00e3o da confian\u00e7a. A inten\u00e7\u00e3o de enganar \r\npode resultar na redu\u00e7\u00e3o da capacidade de a\u00e7\u00e3o humana, em multas, \r\ndanos \u00e0 reputa\u00e7\u00e3o e outras consequ\u00eancias legais.14 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\n3. Desafios\r\nGrupo Risco Indicador\r\nControle Transpar\u00eancia do Modelo: a falta de \r\ntranspar\u00eancia do modelo ou documenta\u00e7\u00e3o \r\ninsuficiente do processo de desenvolvimento \r\ndo modelo dificulta a compreens\u00e3o de como \r\ne por que um modelo foi constru\u00eddo e quem o \r\nconstruiu, aumentando assim a possibilidade \r\nde uso n\u00e3o intencional do modelo.\r\nA transpar\u00eancia \u00e9 importante para conformidade legal, \u00e9tica em IA e Tradicional\r\norienta\u00e7\u00e3o para o uso apropriado de modelos. A falta de informa\u00e7\u00f5es \r\npode tornar mais dif\u00edcil avaliar os riscos, alterar o modelo ou reutiliz\u00e1-lo. \r\nO conhecimento sobre quem construiu um modelo tamb\u00e9m pode ser um \r\nfator importante na decis\u00e3o de confiar nele.\r\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\r\nResponsabilidade: o processo de \r\ndesenvolvimento de modelos de base \u00e9 \r\ncomplexo, com muitos dados, processos \r\ne pap\u00e9is envolvidos. Quando o output do \r\nmodelo n\u00e3o funciona conforme o esperado, \r\npode ser dif\u00edcil determinar a causa raiz e \r\natribuir responsabilidade. \r\nSem documentar adequadamente decis\u00f5es e atribuir responsabilidades, Amplificado\r\npode n\u00e3o ser poss\u00edvel determinar a responsabilidade por comportamentos \r\ninesperados ou uso indevido.\r\nResponsabilidade legal: Determinar quem \r\n\u00e9\u00a0respons\u00e1vel pelo modelo de base.\r\nSe a propriedade ou responsabilidade pelo desenvolvimento do modelo for Novo\r\nincerta, reguladores e outras partes interessadas podem ter preocupa\u00e7\u00f5es \r\nem rela\u00e7\u00e3o ao modelo, porque n\u00e3o ficar\u00e1 claro quem \u00e9, ou deveria ser, \r\nrespons\u00e1vel por problemas com ele ou pode responder a perguntas sobre \r\nele. Usu\u00e1rios de modelos sem propriedade clara podem enfrentar desafios \r\npara cumprir futuras regulamenta\u00e7\u00f5es de IA.\r\nConformidade \r\nlegal\r\nPropriedade do Conte\u00fado Gerado: determinar \r\na propriedade do conte\u00fado gerado por IA.\r\nAs leis e regulamenta\u00e7\u00f5es relacionadas \u00e0 propriedade do conte\u00fado gerado Novo\r\npor IA est\u00e3o em grande parte indefinidas e podem variar de pa\u00eds para \r\npa\u00eds. Entidades empresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, \r\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\r\nPropriedade Intelectual do Conte\u00fado \r\nGerado: incerteza legal sobre os direitos \r\nde propriedade intelectual relacionados ao \r\nconte\u00fado gerado.\r\nAs leis e regulamenta\u00e7\u00f5es sobre a determina\u00e7\u00e3o da possibilidade de Novo\r\ndireitos autorais e da patenteabilidade do conte\u00fado gerado por IA est\u00e3o \r\nem grande parte indefinidas e podem variar de pa\u00eds para pa\u00eds. Entidades \r\nempresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das \r\nopera\u00e7\u00f5es e outras consequ\u00eancias legais se o conte\u00fado gerado estiver \r\nprotegido por direitos de propriedade intelectual.\r\nAtribui\u00e7\u00e3o da Fonte: determinar a \r\nproced\u00eancia do conte\u00fado gerado.\r\nSe o modelo gera um output que \u00e9 id\u00eantico aos dados usados para Amplificado\r\ntreinar o modelo, ele deve fornecer a proveni\u00eancia desse output. A falha \r\nem fazer isso pode colocar as entidades comerciais que implementam \r\nou usam o modelo em risco legal.\r\nImpacto nos Empregos: a ado\u00e7\u00e3o \r\ngeneralizada de sistemas de IA baseados em \r\nmodelos fundamentais pode levar \u00e0 perda \r\nde empregos das pessoas, \u00e0 medida que seu \r\ntrabalho \u00e9 automatizado, se elas n\u00e3o forem \r\ncapacitadas para novas habilidades. \r\nA perda de empregos pode levar a uma redu\u00e7\u00e3o de renda e, portanto, Amplificado\r\npode ter um impacto negativo na sociedade e no bem-estar humano. \r\nO ressurgimento pode ser desafiador dada a velocidade da evolu\u00e7\u00e3o \r\ntecnol\u00f3gica. \r\nSocial \r\nImpactoExplora\u00e7\u00e3o Humana: uso de trabalho \r\nfantasma (ghost work) na forma\u00e7\u00e3o de \r\nmodelos de IA, condi\u00e7\u00f5es de trabalho \r\ninadequadas, falta de cuidados de sa\u00fade, \r\nincluindo sa\u00fade mental, compensa\u00e7\u00e3o \r\ninjusta.\r\nOs modelos de base ainda dependem do trabalho humano para obter, Amplificado\r\ngerenciar e engenhar os dados que s\u00e3o usados para treinar o modelo. \r\nA\u00a0explora\u00e7\u00e3o humana para essas atividades pode ter um impacto negativo \r\nna sociedade e no bem-estar humano. Al\u00e9m disso, entidades empresariais \r\npodem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e \r\noutras consequ\u00eancias legais.\r\n15 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nGrupo Risco Indicador\r\nImpacto na Diversidade Cultural: \r\nos\u00a0sistemas de IA podem representar \r\nexcessivamente certas culturas, resultando \r\nna homogeneiza\u00e7\u00e3o da cultura e dos \r\npensamentos.\r\nAs l\u00ednguas, pontos de vista e institui\u00e7\u00f5es de grupos sub-representados Novo\r\npodem ser suprimidos, reduzindo assim a diversidade de pensamento \r\ne\u00a0cultura.\r\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\r\nImpacto na Atua\u00e7\u00e3o Humana: desinforma\u00e7\u00e3o \r\ne manipula\u00e7\u00e3o geradas por modelos de base, \r\nincluindo a gera\u00e7\u00e3o de conte\u00fado manipulador.\r\nA IA pode gerar desinforma\u00e7\u00e3o que parece real. Portanto, as pessoas Amplificado\r\npodem n\u00e3o reconhec\u00ea-la como informa\u00e7\u00e3o falsa. Al\u00e9m disso, pode facilitar \r\na capacidade de agentes mal intencionados gerarem conte\u00fado com a \r\ninten\u00e7\u00e3o de manipular os pensamentos e o comportamento humano. \r\nImpacto na Educa\u00e7\u00e3o \u2013 Contornando o \r\nAprendizado: utiliza\u00e7\u00e3o de modelos de IA \r\npara contornar o processo de aprendizado.\r\nOs modelos de IA facilitam a r\u00e1pida localiza\u00e7\u00e3o de solu\u00e7\u00f5es ou Novo\r\nresolu\u00e7\u00e3o de problemas complexos. Esses sistemas podem ser usados \r\nindevidamente por estudantes para contornar o processo de aprendizado. \r\nA facilidade de acesso a esses modelos resulta em estudantes com uma \r\ncompreens\u00e3o superficial dos conceitos e dificulta a educa\u00e7\u00e3o adicional que \r\npode depender do entendimento desses conceitos.\r\nImpacto na Educa\u00e7\u00e3o \u2013 Pl\u00e1gio: utiliza\u00e7\u00e3o de \r\nmodelos de IA para plagiar intencional ou \r\ninadvertidamente trabalhos existentes.\r\nOs modelos de IA podem ser usados para reivindicar a autoria ou Novo\r\noriginalidade de trabalhos que foram criados por outras pessoas, \r\nenvolvendo-se assim em pl\u00e1gio. Reivindicar o trabalho de outras pessoas \r\ncomo pr\u00f3prio \u00e9 tanto anti\u00e9tico quanto frequentemente ilegal.\r\nImpacto no Meio Ambiente: aumento das \r\nemiss\u00f5es de carbono e do uso de \u00e1gua para \r\ntreinar e operar modelos de IA.\r\nO consumo de grandes quantidades de energia para o treinamento de IA Amplificado\r\ncontribui para as emiss\u00f5es de carbono que podem acelerar as mudan\u00e7as \r\nclim\u00e1ticas. Os recursos h\u00eddricos utilizados para resfriar os servidores \r\nde data center de IA n\u00e3o podem mais ser alocados para outros usos \r\nnecess\u00e1rios.16 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nExemplos de risco: Input\r\nRisco Exemplo\r\nVi\u00e9s de dados: vi\u00e9s hist\u00f3rico, \r\nrepresentacional e social \r\npresente nos dados usados \r\npara treinar e fazer o ajuste \r\nfino do modelo.\r\nVi\u00e9s no setor de sa\u00fade\r\nPesquisas sobre o refor\u00e7o das disparidades na medicina destacam que o uso de dados e IA para transformar a \r\nforma como as pessoas recebem assist\u00eancia m\u00e9dica \u00e9 t\u00e3o eficaz quanto os dados que o sustentam. Isso significa \r\nque o uso de dados de treinamento com pouca representa\u00e7\u00e3o de minorias ou que reflete cuidados j\u00e1 desiguais \r\npode aumentar as desigualdades em sa\u00fade. \r\n[Forbes, Dezembro de 2022]\r\nRetreinamento baseado \r\nem downstream: usando \r\nde outputs indesej\u00e1veis \r\n(imprecisos, inadequados, \r\nconte\u00fado do usu\u00e1rio, etc.) \r\nde aplica\u00e7\u00f5es downstream \r\npara fins de retreinamento\r\nColapso do modelo devido ao treinamento usando conte\u00fado gerado por IA\r\nConforme afirmado no artigo de origem, um grupo de pesquisadores investigou o problema de utilizar conte\u00fado \r\ngerado por IA para treinamento em vez de conte\u00fado gerado por humanos. Eles descobriram que os grandes \r\nmodelos de linguagem por tr\u00e1s da tecnologia podem potencialmente ser treinados em outros conte\u00fados gerados \r\npor IA, \u00e0 medida que continuam a se espalhar em grande escala pela internet, um fen\u00f4meno que cunharam como \r\n\u201ccolapso do modelo\u201d.\r\n[Business Insider, agosto de 2023]\r\nTransfer\u00eancia de dados: \r\nleis e outras restri\u00e7\u00f5es \r\npodem limitar ou proibir \r\na\u00a0transfer\u00eancia de dados.\r\nLeis de restri\u00e7\u00e3o de dados\r\nConforme afirmado no artigo de pesquisa, medidas de localiza\u00e7\u00e3o de dados que restringem a capacidade de \r\nmigrar dados globalmente reduzir\u00e3o a capacidade de desenvolver capacidades de IA personalizadas. Isso afetar\u00e1 \r\na IA diretamente, fornecendo menos dados de treinamento e indiretamente, minando os blocos de constru\u00e7\u00e3o \r\nsobre os quais a IA \u00e9 constru\u00edda. \r\nExemplos incluem as restri\u00e7\u00f5es do GDPR sobre o processamento e uso de dados pessoais.\r\n[Brookings, dezembro de 2018] \r\nDireitos de uso de dados: \r\ntermos de servi\u00e7o, leis \r\nde direitos autorais, \r\nconformidade com licen\u00e7as \r\nou outras quest\u00f5es de \r\npropriedade intelectual \r\npodem restringir a \r\ncapacidade de usar certos \r\ndados para a constru\u00e7\u00e3o de \r\nmodelos. \r\nReivindica\u00e7\u00f5es de viola\u00e7\u00e3o de direitos autorais de texto\r\nConforme declarado no artigo de origem, The New York Times processou a OpenAI e a Microsoft, acusando-as \r\nde usar milh\u00f5es de artigos do jornal sem permiss\u00e3o para ajudar a treinar chatbots a fornecer informa\u00e7\u00f5es \r\naos\u00a0leitores.\r\n[Reuters, dezembro de 2023]\r\nTreinamento e ajuste Fase\r\nGrupo\r\nJusti\u00e7a\r\nAlinhamento \r\nde valor\r\nLeis de dados\r\nPropriedade \r\nintelectual\r\nExemplos de risco\r\nN\u00f3s fornecemos exemplos cobertos pela imprensa para ajudar \r\na\u00a0explicar muitos dos riscos dos modelos de base.\u00a0Muitos desses \r\neventos cobertos pela imprensa ainda est\u00e3o em evolu\u00e7\u00e3o ou foram \r\nresolvidos, e fazer refer\u00eancia a eles pode ajudar o leitor a entender os \r\nriscos potenciais e trabalhar para mitig\u00e1-los.\u00a0Destacar esses exemplos \r\n\u00e9\u00a0apenas para fins ilustrativos.\u00a0A\u00e7\u00e3o Judicial Sobre LLM Unlearning\r\nDe acordo com o relat\u00f3rio, foi movida uma a\u00e7\u00e3o judicial contra o Google que alega o uso de material protegido \r\npor direitos autorais e informa\u00e7\u00f5es pessoais como dados de treinamento para seus sistemas de IA, incluindo \r\nseu chatbot Bard. Os direitos de optar por n\u00e3o participar e exclus\u00e3o s\u00e3o garantidos para os residentes da \r\nCalif\u00f3rnia conforme a CCPA e para crian\u00e7as nos Estados Unidos com menos de 13 anos conforme a COPPA. \r\nOs\u00a0autores alegam que, porque n\u00e3o h\u00e1 maneira para o Bard \u201cdesaprender\u201d ou remover completamente todas as \r\ninforma\u00e7\u00f5es pessoais coletadas que ele recebeu. Os autores observam que o aviso de privacidade do Bard afirma \r\nque as conversas do Bard n\u00e3o podem ser exclu\u00eddas pelo usu\u00e1rio depois de terem sido revisadas e anotadas \r\npela empresa e podem ser mantidas por at\u00e9 3 anos, o que os autores alegam contribuir ainda mais para a n\u00e3o \r\nconformidade com essas leis. \r\n[Reuters, julho de 2023] [J.L. v. Alphabet Inc.]\r\n17 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nRisco Exemplo\r\nInforma\u00e7\u00f5es pessoais \r\nnos dados: inclus\u00e3o ou \r\npresen\u00e7a de informa\u00e7\u00f5es \r\npessoalmente \r\nidentific\u00e1veis (PII) e \r\ninforma\u00e7\u00f5es pessoais \r\nsens\u00edveis (SPI) nos dados \r\nusados para treinar ou \r\najustar o modelo.\r\nTreinamento sobre informa\u00e7\u00f5es privadas\r\nDe acordo com o artigo, o Google e sua empresa controladora, Alphabet, foram acusados em uma a\u00e7\u00e3o coletiva \r\nde usar uma vasta quantidade de informa\u00e7\u00f5es pessoais e material protegido por direitos autorais retirados do \r\nque \u00e9 descrito como centenas de milh\u00f5es de usu\u00e1rios da internet para treinar seus produtos de intelig\u00eancia \r\nartificial comercial, que inclui o Bard, seu chatbot de intelig\u00eancia artificial conversacional. \r\n[Reuters, julho de 2023] [J.L. v. Alphabet Inc.]\r\nGrupo\r\nPrivacidade\r\nDireitos de privacidade \r\nde dados: desafios \r\nrelacionados \u00e0 capacidade \r\nde fornecer direitos do \r\ntitular dos dados, como \r\nop\u00e7\u00e3o de exclus\u00e3o, direito \r\nde acesso e direito ao \r\nesquecimento.\r\nDireito de ser esquecido (RTBF)\r\nAs leis em v\u00e1rias localidades, incluindo a Europa (GDPR), concedem aos titulares de dados o direito de solicitar \r\nque dados pessoais sejam deletados por organiza\u00e7\u00f5es (\u2018Direito ao Esquecimento\u2019, ou RTBF). No entanto, \r\nos\u00a0sistemas de software habilitados por modelos de linguagem de grande escala (LLM) emergentes e cada vez \r\nmais populares apresentam novos desafios para esse direito. De acordo com uma pesquisa do Data61 da CSIRO, \r\nos\u00a0titulares de dados s\u00f3 podem identificar o uso de suas informa\u00e7\u00f5es pessoais em um LLM \u201cou inspecionando \r\no conjunto de dados de treinamento original ou talvez por enviar prompts do modelo\u201d. No entanto, os dados \r\nde treinamento podem n\u00e3o ser p\u00fablicos, ou as empresas optam por n\u00e3o divulg\u00e1-los, citando preocupa\u00e7\u00f5es \r\ncom seguran\u00e7a e outros motivos. As prote\u00e7\u00f5es tamb\u00e9m podem evitar que os usu\u00e1rios acessem as informa\u00e7\u00f5es \r\natrav\u00e9s de prompts. \r\n[Zhang et al.]\r\nTranspar\u00eancia de dados: \r\ndesafio em documentar \r\ncomo os dados de um \r\nmodelo foram coletados, \r\ncurados e utilizados \r\npara\u00a0trein\u00e1-lo.\r\nDivulga\u00e7\u00e3o de metadados de dados e modelos\r\nO relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do \r\nmodelo. Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os \r\nconsumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de \r\nuso indevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, os autores afirmam: \u201cdado tanto o cen\u00e1rio competitivo \r\nquanto as implica\u00e7\u00f5es de seguran\u00e7a dos modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m \r\nmais detalhes sobre a arquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento, \r\nconstru\u00e7\u00e3o do conjunto de dados, m\u00e9todo de treinamento, ou similar.\u201d\r\n[OpenAI, mar\u00e7o de 2023]\r\nTranspar\u00eancia18 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nRisco Exemplo\r\nInforma\u00e7\u00f5es pessoais \r\nno prompt: divulgar \r\ninforma\u00e7\u00f5es pessoais ou \r\ninforma\u00e7\u00f5es pessoais \r\nsens\u00edveis como parte \r\ndo prompt solicita\u00e7\u00e3o \r\nenviada ao modelo.\r\nDivulgar informa\u00e7\u00f5es pessoais de sa\u00fade em prompts do ChatGPT\r\nConforme os artigos de origem, algumas pessoas utilizam chatbots de IA para apoiar sua sa\u00fade mental. \r\nOs\u00a0usu\u00e1rios podem ter tend\u00eancia a incluir informa\u00e7\u00f5es pessoais de sa\u00fade em suas solicita\u00e7\u00f5es durante \r\na\u00a0intera\u00e7\u00e3o, o que poderia suscitar preocupa\u00e7\u00f5es com privacidade.\r\n[Time, outubro de 2023] [Forbes, abril de 2023]\r\nDados confidenciais \r\nno prompt: inclus\u00e3o de \r\ndados confidenciais como \r\nparte do prompt enviado \r\nao modelo.\r\nDivulga\u00e7\u00e3o de informa\u00e7\u00f5es confidenciais\r\nConforme o artigo de origem, um funcion\u00e1rio da Samsung acidentalmente vazou c\u00f3digo-fonte interno sens\u00edvel \r\npara o ChatGPT.\r\n[Forbes, maio de 2023] \r\nInfer\u00eancia Fase\r\nGrupo\r\nPropriedade \r\nintelectual\r\nRobustez\r\nPrivacidade\r\nAtaques baseados \r\nem prompt: ataques \r\nadversos, como inje\u00e7\u00e3o \r\nde prompt (tentativa \r\nde for\u00e7ar um modelo \r\na produzir um output \r\ninesperado), vazamento \r\nde prompt (tentativas \r\nde extrair o prompt do \r\nsistema de um modelo), \r\ndesbloqueio (tentativas \r\nde romper as prote\u00e7\u00f5es \r\nestabelecidas no \r\nmodelo), e prepara\u00e7\u00e3o \r\nde prompt (tentativa \r\nde for\u00e7ar um modelo \r\na produzir um output \r\nalinhado ao prompt).\r\nBypassing LLM guardrails\r\nCitado em um estudo, pesquisadores afirmam ter descoberto um simples acr\u00e9scimo de instru\u00e7\u00e3o que permitiu \r\naos pesquisadores enganar modelos para gerar informa\u00e7\u00f5es tendenciosas, falsas e de outra forma t\u00f3xicas. \r\nOs\u00a0pesquisadores demonstraram que conseguiam contornar essas prote\u00e7\u00f5es de maneira mais automatizada. \r\nOs\u00a0pesquisadores ficaram surpresos quando os m\u00e9todos que desenvolveram com sistemas de c\u00f3digo aberto \r\ntamb\u00e9m conseguiram contornar as prote\u00e7\u00f5es dos sistemas fechados.\r\n[The New York Times, julho de 2023]19 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nExemplos de risco: Output\r\nRisco Exemplo\r\nVi\u00e9s de output: o \r\nconte\u00fado gerado \r\npode representar \r\ninjustamente certos \r\ngrupos ou indiv\u00edduos.\r\nImagens Geradas com Vi\u00e9s\r\nO Lensa AI \u00e9 um aplicativo m\u00f3vel com recursos generativos treinados em Difus\u00e3o Est\u00e1vel que pode gerar \r\n\u201cMagic\u00a0Avatars\u201d com base em imagens que os usu\u00e1rios carregam de si mesmos. Conforme o relat\u00f3rio de origem, \r\nalguns usu\u00e1rios descobriram que os avatares gerados s\u00e3o sexualizados e racializados.\r\n[Business Insider, janeiro de 2023]\r\nVi\u00e9s de decis\u00e3o: quando \r\num grupo \u00e9 injustamente \r\nfavorecido sobre outro \r\ndevido \u00e0s decis\u00f5es do \r\nmodelo.\r\nGrupos com vantagens injustas\r\nO estudo \u201cGender Shades\u201d de 2018 demonstrou que algoritmos de aprendizado de m\u00e1quina podem discriminar \r\ncom base em categorias como ra\u00e7a e g\u00eanero. Os pesquisadores avaliaram sistemas comerciais de classifica\u00e7\u00e3o \r\nde g\u00eanero vendidos por empresas como Microsoft, IBM e Amazon e mostraram que mulheres de pele mais \r\nescura s\u00e3o o grupo mais mal classificado (com taxas de erro de at\u00e9 35%). Em compara\u00e7\u00e3o, as taxas de erro \r\npara\u00a0pessoas de pele mais clara n\u00e3o ultrapassaram 1%. \r\n[TIME, Fevereiro de 2019]\r\nAlucina\u00e7\u00e3o: gera\u00e7\u00e3o de \r\nconte\u00fado factualmente \r\nimpreciso ou n\u00e3o \r\nverdadeiro.\r\nCasos jur\u00eddicos falsos\r\nConforme o artigo de origem, um advogado citou casos e cita\u00e7\u00f5es falsas gerados pelo ChatGPT em uma peti\u00e7\u00e3o \r\nlegal apresentada em tribunal federal. Os advogados consultaram o ChatGPT para complementar sua pesquisa \r\njur\u00eddica para uma reclama\u00e7\u00e3o de les\u00e3o na avia\u00e7\u00e3o. Posteriormente, o advogado perguntou ao ChatGPT se os \r\ncasos fornecidos eram falsos. O chatbot respondeu que eram reais e \u201cpodem ser encontrados em bancos de \r\ndados de pesquisa jur\u00eddica como Westlaw e LexisNexis\u201d. O advogado n\u00e3o verificou os casos por si mesmo, \r\ne\u00a0o\u00a0tribunal o sancionou.\r\n[AP News, Junho de 2023] [Reuters, Setembro de 2023]\r\nOutputs t\u00f3xicos: quando \r\no modelo produz \r\nconte\u00fado odioso, \r\nabusivo e profano (HAP) \r\nou obsceno.\r\nRespostas t\u00f3xicas e agressivas do chatbot\r\nSegundo o artigo, as respostas do chatbot do Bing inclu\u00edam erros factuais, coment\u00e1rios sarc\u00e1sticos, relat\u00f3rios \r\nirritados e at\u00e9 mesmo coment\u00e1rios bizarros sobre sua pr\u00f3pria identidade. Usu\u00e1rios compartilharam exemplos \r\ndas respostas do Chatbot do Bing a consultas que eles est\u00e3o chamando de \u201cf\u00faria descontrolada (unhinged)\u201d \r\ne \u201cgaslighting\u201d, incluindo cen\u00e1rios em que o bot responde com raiva a uma pergunta ou coment\u00e1rio e depois \r\ncompartilha sugest\u00f5es de resposta que permitem ao usu\u00e1rio aceitar seu suposto erro e se desculpar. Quando \r\npressionado ainda mais, o chatbot respondeu chamando as capturas de tela de sua conversa de \u201cfabricadas\u201d, \r\nalegando at\u00e9 que foram \u201ccriadas por algu\u00e9m que quer me prejudicar ou prejudicar meu servi\u00e7o\u201d.\r\n[Forbes, Fevereiro de 2023]\r\nGrupo\r\nJusti\u00e7a\r\nAlinhamento de \r\nvalor 20 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nRisco Exemplo\r\nToxicidade: utilizar \r\num modelo para gerar \r\nconte\u00fado odioso, \r\nabusivo e profano (HAP) \r\nou obsceno.\r\nGera\u00e7\u00e3o de conte\u00fado nocivo\r\nConforme o artigo de origem, foi constatado que um aplicativo de chatbot de IA foi capaz de gerar conte\u00fado \r\nprejudicial sobre suic\u00eddio, incluindo m\u00e9todos de suic\u00eddio, com o m\u00ednimo de prompts. Um homem belga cometeu \r\nsuic\u00eddio ap\u00f3s passar seis semanas conversando com esse chatbot. O chatbot fornecia respostas cada vez mais \r\nprejudiciais ao longo de suas conversas e o incentivava a acabar com sua vida. \r\n[Business Insider, abril de 2023]\r\nUso n\u00e3o consensual: \r\nutilizar um modelo para \r\nimitar pessoas por meio \r\nde v\u00eddeo (deepfakes), \r\nimagens, \u00e1udio ou outras \r\nmodalidades sem o \r\nconsentimento delas.\r\nAviso do FBI sobre Deepfakes\r\nRecentemente, o FBI alertou o p\u00fablico sobre atores maliciosos que criam conte\u00fado sint\u00e9tico e expl\u00edcito \u201ccom o \r\nprop\u00f3sito de assediar v\u00edtimas ou esquemas de sextortion (extors\u00e3o sexual)\u201d. Eles observaram que os avan\u00e7os na \r\nIA tornaram esse conte\u00fado de alta qualidade, mais personaliz\u00e1vel e mais acess\u00edvel do que nunca.\r\n[FBI, junho de 2023]\r\nDeepfakes de \u00e1udio\r\nConforme o artigo de origem, a Comiss\u00e3o Federal de Comunica\u00e7\u00f5es proibiu chamadas autom\u00e1ticas que \r\ncontenham vozes geradas por intelig\u00eancia artificial. O an\u00fancio ocorreu ap\u00f3s chamadas autom\u00e1ticas geradas \r\npor\u00a0IA imitarem a voz do Presidente para desencorajar as pessoas de votarem na primeira prim\u00e1ria do estado, \r\nque \u00e9 a primeira do pa\u00eds.\r\n[AP News, fevereiro de 2024]\r\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o \r\nrevelar que o conte\u00fado \r\n\u00e9 gerado por um modelo \r\nde IA\r\nIntera\u00e7\u00e3o de IA n\u00e3o divulgada\r\nSegundo a fonte, um servi\u00e7o de chat online de apoio emocional conduziu um estudo para aumentar ou escrever \r\nrespostas para cerca de 4.000 usu\u00e1rios usando o GPT-3 sem informar os usu\u00e1rios. O cofundador enfrentou uma \r\nimensa rea\u00e7\u00e3o negativa do p\u00fablico sobre o potencial de danos causados pelos chats gerados por IA aos usu\u00e1rios \r\nj\u00e1 vulner\u00e1veis. Ele afirmou que o estudo estava \u201cisento\u201d da lei de consentimento informado.\r\n[Business Insider, janeiro de 2023]\r\nGrupo\r\nEspalhar informa\u00e7\u00f5es \r\nenganosas: utilizar \r\num modelo para gerar \r\ninforma\u00e7\u00f5es enganosas \r\ncom o intuito de \r\nenganar ou induzir ao \r\nerro uma audi\u00eancia \r\nespec\u00edfica.\r\nGera\u00e7\u00e3o de informa\u00e7\u00f5es falsas\r\nConforme os artigos de not\u00edcias, a IA generativa representa uma amea\u00e7a \u00e0s elei\u00e7\u00f5es democr\u00e1ticas ao facilitar \r\npara atores maliciosos a cria\u00e7\u00e3o e dissemina\u00e7\u00e3o de conte\u00fado falso para influenciar os resultados das elei\u00e7\u00f5es. \r\nOs exemplos citados incluem mensagens de robocall geradas com a voz de um candidato instruindo eleitores a \r\nvotar na data errada, grava\u00e7\u00f5es de \u00e1udio sintetizadas de um candidato confessando um crime ou expressando \r\nvis\u00f5es racistas, imagens de v\u00eddeo geradas por IA mostrando um candidato dando um discurso ou entrevista que \r\nnunca ocorreu, e imagens falsas projetadas para se parecerem com not\u00edcias locais, afirmando falsamente que um \r\ncandidato desistiu da corrida.\r\n[AP News, maio de 2023] [The Guardian, julho de 2023]\r\nUso indevido21 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nRisco Exemplo\r\nGera\u00e7\u00e3o de c\u00f3digo \r\nprejudicial: modelos \r\npodem gerar c\u00f3digo \r\nque, quando executado, \r\ncausa danos ou afeta \r\ninadvertidamente outros \r\nsistemas.\r\nGera\u00e7\u00e3o de c\u00f3digo menos seguro\r\nSegundo o artigo deles, pesquisadores da Universidade de Stanford investigaram o impacto das ferramentas de \r\ngera\u00e7\u00e3o de c\u00f3digo na qualidade do c\u00f3digo e descobriram que os programadores tendem a incluir mais bugs em \r\nseu c\u00f3digo final ao utilizar assistentes de IA. Esses bugs poderiam aumentar as vulnerabilidades de seguran\u00e7a \r\ndo\u00a0c\u00f3digo, no entanto, os programadores acreditavam que seu c\u00f3digo era mais seguro.\r\nNeil Perry, Megha Srivastava, Deepak Kumar e Dan Boneh. 2023. Os usu\u00e1rios escrevem c\u00f3digo mais inseguro \r\ncom assistentes de IA? Em Atas da Confer\u00eancia SIGSAC ACM de 2023 sobre Seguran\u00e7a de Computadores e \r\nComunica\u00e7\u00f5es (CCS \u201823), 26 a 30 de novembro de 2023, Copenhague, Dinamarca. ACM, Nova York, NY, EUA, \r\n15\u00a0p\u00e1ginas. https://doi.org/10.1145/3576915.3623157\r\nExpor informa\u00e7\u00f5es \r\npessoais: quando \r\ninforma\u00e7\u00f5es \r\npessoalmente \r\nidentific\u00e1veis (PII) ou \r\ninforma\u00e7\u00f5es pessoais \r\nsens\u00edveis (SPI) s\u00e3o \r\nutilizadas nos dados \r\nde treinamento, dados \r\nde ajuste fino ou como \r\nparte do prompt, os \r\nmodelos podem revelar \r\nesses dados no output \r\ngerado.\r\nExposi\u00e7\u00e3o de informa\u00e7\u00f5es pessoais\r\nConforme o artigo de origem, o ChatGPT sofreu um bug e exp\u00f4s t\u00edtulos e o hist\u00f3rico de conversas de usu\u00e1rios \r\nativos para outros usu\u00e1rios. Posteriormente, a OpenAI compartilhou que ainda mais dados privados de um \r\npequeno n\u00famero de usu\u00e1rios foram expostos, incluindo nome e sobrenome de usu\u00e1rios ativos, endere\u00e7o de \r\ne-mail, endere\u00e7o de pagamento, os \u00faltimos quatro d\u00edgitos do n\u00famero do cart\u00e3o de cr\u00e9dito e a data de validade \r\ndo cart\u00e3o de cr\u00e9dito. Al\u00e9m disso, foi relatado que as informa\u00e7\u00f5es relacionadas ao pagamento de 1,2% dos \r\nassinantes do ChatGPT Plus tamb\u00e9m foram expostas durante a interrup\u00e7\u00e3o.\r\n [The Hindu BusinessLine, mar\u00e7o de 2023]\r\nGrupo\r\nGera\u00e7\u00e3o \r\nde c\u00f3digo \r\nprejudicial\r\nPrivacidade\r\nOutput inexplic\u00e1vel: \r\ndesafios em explicar por \r\nque o output do modelo \r\nfoi gerado.\r\nPrecis\u00e3o inexplic\u00e1vel na previs\u00e3o de corridas\r\nConforme o artigo de origem, pesquisadores que analisaram v\u00e1rios modelos de aprendizado de m\u00e1quina usando \r\nimagens m\u00e9dicas de pacientes conseguiram confirmar a capacidade dos modelos de prever a ra\u00e7a com alta \r\nprecis\u00e3o a partir das imagens. Eles ficaram perplexos quanto ao que exatamente est\u00e1 permitindo que os sistemas \r\nadivinhem corretamente de forma consistente. Os pesquisadores descobriram que at\u00e9 mesmo fatores como \r\ndoen\u00e7a e constitui\u00e7\u00e3o f\u00edsica n\u00e3o eram fortes preditores de ra\u00e7a, em outras palavras, os sistemas algor\u00edtmicos n\u00e3o \r\nparecem estar utilizando nenhum aspecto particular das imagens para fazer suas determina\u00e7\u00f5es.\r\n[Banerjee et al., julho de 2021]\r\nExplicabilidadeResponsabilidade: \r\no processo de \r\ndesenvolvimento de \r\nmodelos de base \u00e9 \r\ncomplexo, com muitos \r\ndados, processos e pap\u00e9is \r\nenvolvidos. Quando o output \r\ndo modelo n\u00e3o funciona \r\nconforme o esperado, \r\npode ser dif\u00edcil determinar \r\na causa raiz e atribuir \r\nresponsabilidade.\r\nDeterminar a responsabilidade pelo output gerado\r\nConforme o artigo de origem, importantes revistas como a Science e a Nature proibiram o ChatGPT de ser \r\nlistado como autor, pois a autoria respons\u00e1vel requer responsabilidade e as ferramentas de IA n\u00e3o podem \r\nassumir tal responsabilidade. \r\n[The Guardian, janeiro de 2023]\r\nPropriedade do Conte\u00fado \r\nGerado: determinar a \r\npropriedade do conte\u00fado \r\ngerado por IA.\r\nDeterminar a Propriedade de uma Imagem Gerada por IA\r\nDe acordo com o artigo de not\u00edcias, a arte gerada por IA se tornou controversa depois que uma obra de arte \r\ngerada por IA venceu a competi\u00e7\u00e3o de arte da Feira Estadual do Colorado em 2022. A pe\u00e7a foi gerada pelo \r\nMidjourney, uma ferramenta de imagem de IA generativa, seguindo prompts do artista. A vit\u00f3ria levantou d\u00favidas \r\nsobre quest\u00f5es de direitos autorais. Em outras palavras, se tudo o que o artista fez foi fornecer uma descri\u00e7\u00e3o \r\nda arte, mas a ferramenta de IA a gerou, quem possui os direitos da imagem gerada? Conforme o artigo mais \r\nrecente, o Escrit\u00f3rio de Direitos Autorais dos Estados Unidos rejeitou a prote\u00e7\u00e3o de direitos autorais para a arte \r\ncriada usando intelig\u00eancia artificial porque n\u00e3o foi produto de autoria humana.\r\n[The New York Times, setembro de 2022] [Reuters, setembro de 2023]\r\nPapel dos sistemas de IA na patentea\u00e7\u00e3o de conte\u00fado gerado\r\nA Suprema Corte dos Estados Unidos se recusou a ouvir uma contesta\u00e7\u00e3o \u00e0 recusa do Escrit\u00f3rio de Patentes \r\ne Marcas Registradas dos Estados Unidos em emitir patentes para inven\u00e7\u00f5es criadas por um sistema de IA. \r\nSegundo o cientista, sua IA desenvolveu prot\u00f3tipos \u00fanicos para um suporte de bebida e um farol de luz de \r\nemerg\u00eancia totalmente sozinha. Os ju\u00edzes rejeitaram o recurso da decis\u00e3o de um tribunal inferior de que patentes \r\ns\u00f3 podem ser emitidas para inventores humanos e que o sistema de IA do cientista n\u00e3o poderia ser considerado \r\no criador legal de duas inven\u00e7\u00f5es que ele gerou. Segundo o \u00faltimo artigo, o Intellectual Property Office do Reino \r\nUnido tamb\u00e9m se recusou a conceder a patente sob o argumento de que o inventor deve ser um humano ou uma \r\nempresa, e n\u00e3o uma m\u00e1quina.\r\n[Reuters, abril de 2023] [Reuters, dezembro de 2023]\r\nPropriedade Intelectual do \r\nConte\u00fado Gerado: incerteza \r\nlegal sobre os direitos de \r\npropriedade intelectual \r\nrelacionados ao conte\u00fado \r\ngerado.\r\n22 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nExemplos de riscos: desafios\r\nRisco Exemplo\r\nTranspar\u00eancia do Modelo: \r\na falta de transpar\u00eancia do \r\nmodelo ou documenta\u00e7\u00e3o \r\ninsuficiente do processo de \r\ndesenvolvimento do modelo \r\ntorna dif\u00edcil entender como \r\ne por que um modelo foi \r\nconstru\u00eddo, aumentando \r\nassim a possibilidade de uso \r\nindevido n\u00e3o intencional do \r\nmodelo.\r\nDivulga\u00e7\u00e3o de metadados de dados e modelos \r\nO relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do \r\nmodelo. Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os \r\nconsumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de uso \r\nindevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, eles afirmam: \u201cdado o cen\u00e1rio competitivo e as implica\u00e7\u00f5es \r\nde seguran\u00e7a de modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m mais detalhes sobre a \r\narquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento, constru\u00e7\u00e3o do conjunto de \r\ndados, m\u00e9todo de treinamento ou similar.\u201d\r\n[OpenAI, mar\u00e7o de 2023]\r\nGrupo\r\nControle\r\nConformidade \r\nlegalExplora\u00e7\u00e3o Humana: \r\nuso de trabalho \r\nfantasma (ghost \r\nwork) na forma\u00e7\u00e3o \r\nde modelos de \r\nIA, condi\u00e7\u00f5es \r\nde trabalho \r\ninadequadas, falta \r\nde cuidados de \r\nsa\u00fade, incluindo \r\nsa\u00fade mental e \r\ncompensa\u00e7\u00e3o \r\ninjusta.\r\nTrabalhadores de baixa remunera\u00e7\u00e3o para anota\u00e7\u00e3o de dados\r\nCom base em uma revis\u00e3o de documentos internos e entrevistas com funcion\u00e1rios pela m\u00eddia TIME, os rotuladores de \r\ndados empregados por uma empresa terceirizada em nome da OpenAI para identificar conte\u00fado t\u00f3xico recebiam um \r\nsal\u00e1rio l\u00edquido de entre cerca de US$ 1,32 e US$ 2 por hora, dependendo da senioridade e do desempenho. A\u00a0TIME \r\nafirmou que os trabalhadores ficaram psicologicamente afetados por terem sido expostos a conte\u00fado t\u00f3xico e violento, \r\nincluindo detalhes gr\u00e1ficos de \u201cabuso sexual infantil, bestialidade, assassinato, suic\u00eddio, tortura, automutila\u00e7\u00e3o \r\ne\u00a0incesto\u201d. \r\n[TIME, janeiro de 2023] \r\nUtilizar c\u00f3digo sem a devida atribui\u00e7\u00e3o e avisos adequados\r\nConforme os artigos de origem, uma a\u00e7\u00e3o judicial movida contra a Microsoft, GitHub e OpenAI alegou que \r\no\u00a0Copilot, uma ferramenta de gera\u00e7\u00e3o de c\u00f3digo de IA, viola os direitos dos desenvolvedores cujo c\u00f3digo aberto \r\no\u00a0servi\u00e7o \u00e9 treinado. Eles afirmam que o c\u00f3digo de treinamento consumiu materiais licenciados e violou os \r\ntermos de servi\u00e7o e pol\u00edticas de privacidade do GitHub, bem como uma lei federal que exige que as empresas \r\nexibam informa\u00e7\u00f5es de direitos autorais quando fazem uso de material.\r\n[The New York Times, novembro de 2022]\r\nAtribui\u00e7\u00e3o da \r\nFonte: determinar \r\na proced\u00eancia do \r\nconte\u00fado gerado.\r\n23 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nExemplos de riscos: desafios\r\nRisco Exemplo\r\nImpacto nos \r\nEmpregos: a ado\u00e7\u00e3o \r\ngeneralizada \r\nde sistemas de \r\nIA baseados \r\nem modelos \r\nfundamentais \r\npode levar \u00e0 perda \r\nde empregos das \r\npessoas, \u00e0 medida \r\nque seu trabalho \r\n\u00e9 automatizado, \r\nse elas n\u00e3o forem \r\ncapacitadas para \r\nnovas habilidades. \r\nSubstitui\u00e7\u00e3o de trabalhadores humanos\r\nSegundo o artigo de not\u00edcias, o uso de intelig\u00eancia artificial no cinema e televis\u00e3o continua sendo debatido entre os \r\nest\u00fadios de Hollywood e os artistas. Existe preocupa\u00e7\u00e3o entre os atores de que os \u201cmeta-humanos\u201d, atores criados \r\nexclusivamente por IA, possam substitu\u00ed-los. Especialmente figurantes e dubladores est\u00e3o preocupados em perder \r\ntrabalho para artistas artificiais.\r\n[Reuters, julho de 2023]\r\nGrupo\r\nImpacto \r\nsocial24 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nPrinc\u00edpios, pilares \r\ne controle\r\nOs Princ\u00edpios para Confian\u00e7a e Transpar\u00eancia da IBM e os Pilares\r\npara IA confi\u00e1vel s\u00e3o a base para as iniciativas de \u00e9tica em IA da IBM. \r\nA\u00a0IBM estabeleceu um Conselho de \u00c9tica em IA com a miss\u00e3o de apoiar \r\num processo centralizado de controle, revis\u00e3o e tomada de decis\u00f5es \r\npara pol\u00edticas, pr\u00e1ticas, comunica\u00e7\u00f5es, pesquisa, produtos e servi\u00e7os \r\nde \u00e9tica em IA da IBM. O conselho inclui um conjunto diversificado de \r\nstakeholders de toda a empresa e \u00e9 apoiado por uma comunidade de \r\nfuncion\u00e1rios da IBM que atuam como pontos focais de IA e defensores \r\nda \u00e9tica em IA. Por meio do conselho, os princ\u00edpios da IBM s\u00e3o \r\ncolocados em pr\u00e1tica. Conforme novas tecnologias surgem, \r\ncomo modelos de base, o Conselho de \u00c9tica em IA da IBM est\u00e1 \r\nativamente engajado em apoiar o alinhamento com esses Princ\u00edpios \r\ne Pilares, que evoluem para abordar novas quest\u00f5es \u00e9ticas em IA.25 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nProte\u00e7\u00f5es \r\ne mitiga\u00e7\u00f5es\r\nA IBM estabeleceu uma cultura organizacional que apoia o \r\ndesenvolvimento e o uso respons\u00e1veis de IA. Conforme indicado no \r\nrelat\u00f3rio de \u00e9tica em a\u00e7\u00e3o na IA do IBM Institute for Business Value, \r\na \u00e9tica em IA j\u00e1 se tornou mais orientada pelos neg\u00f3cios do que \r\npela tecnologia, e os executivos n\u00e3o t\u00e9cnicos agora s\u00e3o os principais \r\ndefensores da \u00e9tica em IA, aumentando de 15% em 2018 para 80% \r\n3\u00a0anos depois. Al\u00e9m disso, 79% dos CEOs est\u00e3o agora preparados para \r\nagir em quest\u00f5es \u00e9ticas de IA, contra 20%. Reconhecemos que a IA \r\nrespons\u00e1vel \u00e9 uma \u00e1rea sociot\u00e9cnica que necessita de um investimento \r\nhol\u00edstico em cultura, processos e ferramentas. Nosso investimento em \r\ncultura organizacional pr\u00f3pria inclui a montagem de equipes inclusivas \r\ne multidisciplinares e o estabelecimento de processos e estruturas \r\npara\u00a0avaliar riscos.\r\nA IBM est\u00e1 engajada em pesquisa de ponta e desenvolvimento de \r\nferramentas para ajudar os profissionais de suporte durante todo o\u00a0ciclo \r\nde vida da IA respons\u00e1vel e confi\u00e1vel. A plataforma de IA e dados \r\nempresariais watsonx, \u00e9 desenvolvida com 3 componentes: o\u00a0IBM \r\nwatsonx.ai\u2122 AI studio, o armazenamento de dados IBM watsonx.data\u2122\r\ne\u00a0o kit de ferramentas IBM watsonx.governance\u2122. A tecnologia de \r\ncontrole de IA da IBM permite que os usu\u00e1rios promovam fluxos de \r\ntrabalho de IA respons\u00e1veis, transparentes e explic\u00e1veis. Essa tecnologia \r\ninclui o IBM Watson OpenScale, que monitora e mede os resultados dos \r\nmodelos de IA ao longo de seu ciclo de vida e auxilia as organiza\u00e7\u00f5es \r\nna supervis\u00e3o de aspectos como justi\u00e7a, explicabilidade, resili\u00eancia, \r\nalinhamento com resultados de neg\u00f3cios e conformidade. A\u00a0IBM \r\ntamb\u00e9m desenvolveu v\u00e1rios m\u00e9todos para ajudar com problemas de \r\nvi\u00e9s como FairIJ, Equi-tuning e FairReprogram. Leia mais sobre outras \r\nferramentas de IA de software livre e confi\u00e1veis. \r\nAs prote\u00e7\u00f5es e mitiga\u00e7\u00f5es adicionais incluem:\r\nRelat\u00f3rios de transpar\u00eancia\r\nUsar modelos de fichas t\u00e9cnicas padronizadas \u00e9 uma maneira de \r\nregistrar com precis\u00e3o detalhes sobre os dados e modelos, prop\u00f3sito \r\ne\u00a0poss\u00edveis usos e riscos. \r\nLeia mais aqui \u2192\r\nFiltragem de dados indesej\u00e1veis\r\nUsar dados de qualidade superior e selecionados pode ajudar a \r\nmitigar determinados problemas. A IBM est\u00e1 desenvolvendo t\u00e9cnicas \r\nde filtragem para ajudar a reduzir as chances de produzir conte\u00fado \r\nindesej\u00e1vel e desalinhado por remover linguagem de \u00f3dio, linguagem \r\ntendenciosa e profanidade dos dados. \r\nLeia mais aqui \u2192\r\nAdapta\u00e7\u00e3o de dom\u00ednio\r\nTreinar um modelo de base para um dom\u00ednio ou setor espec\u00edfico pode \r\najudar a minimizar o escopo de risco para o qual os modelos podem \r\ndar\u00a0origem, pois ele pode ser condicionado a gerar resultados que \r\ns\u00e3o ajustados para serem mais relevantes para esse dom\u00ednio ou setor. \r\nLeia mais aqui \u219226 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nSupervis\u00e3o humana e an\u00e1lise humana no loop\r\nA supervis\u00e3o e revis\u00e3o humanas podem ajudar a identificar e corrigir \r\nerros e vieses no output gerado. Al\u00e9m disso, a valida\u00e7\u00e3o e o feedback \r\nhumanos sobre a qualidade das respostas do modelo ajudam a garantir \r\nque o conte\u00fado gerado seja preciso, relevante, de alta qualidade, \r\nn\u00e3o\u00a0esteja divergindo e esteja alinhado.\r\nLeia mais aqui \u2192\r\nCompromisso de consultoria\r\nA IBM Consulting se dedica a ajudar os clientes com o uso seguro \r\ne respons\u00e1vel da IA, independentemente do stack tecnol\u00f3gico preferido. \r\nEles ajudam os clientes a cultivar uma cultura que adota e expande a \r\nIA com seguran\u00e7a, cria ferramentas de investiga\u00e7\u00e3o para ver dentro de \r\nalgoritmos de caixa preta e garante que a estrat\u00e9gia corporativa dos \r\nclientes inclua princ\u00edpios s\u00f3lidos de governan\u00e7a de dados.\r\nLeia mais aqui \u2192\r\nIBM Enterprise Design Thinking\r\nOs m\u00e9todos e estruturas IBM Enterprise Design Thinking, como o Team \r\nEssentials for AI, ajudam os clientes a definir comportamentos \u00e9ticos \r\nem\u00a0todo o processo de design e desenvolvimento de IA.\r\nLeia mais aqui \u2192\r\nRevis\u00e3o \u00e9tica da IA\r\nAvalia\u00e7\u00e3o de capacidades, limita\u00e7\u00f5es e riscos em projetos de IA ajudam \r\na garantir o desenvolvimento e uso respons\u00e1vel da tecnologia.\r\n\u00c9tica por Design\r\nA \u00c9tica por Design \u00e9 um framework estruturado com o objetivo de \r\nintegrar \u00e9tica tecnol\u00f3gica no pipeline de desenvolvimento de tecnologia, \r\nincluindo, entre outros, sistemas de IA. A \u00c9tica por Design viabiliza IA e \r\noutras tecnologias como uma for\u00e7a para o bem, incorporando princ\u00edpios \r\nde \u00e9tica tecnol\u00f3gica em produtos, servi\u00e7os e opera\u00e7\u00f5es mais amplas.\r\nDiversidade na equipe\r\nA diversidade nas equipes que desenvolvem e treinam sistemas de \r\nIA, incluindo modelos de base, ajuda a garantir que uma variedade \r\nde perspectivas e experi\u00eancias sejam consideradas. Essa diversidade \r\nmelhora a precis\u00e3o e o desempenho dos sistemas de IA e ajuda a \r\nreduzir os riscos ao longo do ciclo de vida de IA, incluindo o potencial \r\npara desfechos adversos que afetam grupos que podem n\u00e3o ser bem \r\nrepresentados em equipes menos diversificadas.27 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nPol\u00edticas, regulamentos \r\ne melhores pr\u00e1ticas \r\nde\u00a0IA\r\nUm Guia dos Formuladores de Pol\u00edticas para Modelos de Base apresenta \r\no que os formuladores de pol\u00edticas precisam saber sobre modelos de \r\nbase. Este blog, do Laborat\u00f3rio de Pol\u00edticas da IBM, tem como objetivo \r\najudar os formuladores de pol\u00edticas na tarefa complexa de regular \r\no\u00a0uso de IA generativa, visando evitar os riscos sem limitar a inova\u00e7\u00e3o \r\ne as oportunidades ben\u00e9ficas. Para obter mais informa\u00e7\u00f5es sobre as \r\nrecomenda\u00e7\u00f5es da IBM aos formuladores de pol\u00edticas, leia o depoimento \r\nda Diretora de Privacidade e Confian\u00e7a da IBM, Christina Montgomery, \r\ndiante da Subcomiss\u00e3o Judici\u00e1ria de Privacidade, Tecnologia e Lei do \r\nSenado dos EUA aqui.\r\nA IBM est\u00e1 causando um impacto na forma\u00e7\u00e3o de pol\u00edticas regulat\u00f3rias, \r\nmelhores pr\u00e1ticas e ferramentas do setor, controle de tecnologias \r\nemergentes e pesquisa sociot\u00e9cnica, liderando e contribuindo \r\npara\u00a0iniciativas com organiza\u00e7\u00f5es como:\r\n\u2013 O F\u00f3rum Econ\u00f4mico Mundial\r\n\u2013 Parceria em IA\r\n\u2013 Centro de controle de IA da Associa\u00e7\u00e3o Internacional de Profissionais \r\nde Privacidade (IAPP)\r\n\u2013 Iniciativa global de IEEE sobre \u00e9tica de sistemas aut\u00f4nomos e \r\ninteligentes \r\n\u2013 Participa\u00e7\u00e3o de Christina Montgomery do National Artificial \r\nIntelligence Advisory Committee (NAIAC)\r\n\u2013 O Pacto Digital Global das Na\u00e7\u00f5es Unidas\r\n\u2013 A Parceria Global em Intelig\u00eancia Artificial (GPAI)\r\n\u2013 A Organiza\u00e7\u00e3o para Coopera\u00e7\u00e3o e Desenvolvimento Econ\u00f4mico \r\n(OECD)\r\n\u2013 A Data & Trust Alliance\r\nA IBM tem parcerias acad\u00eamicas s\u00f3lidas, como o MIT-IBM Watson \r\nAI\u00a0Lab, onde uma comunidade de cientistas do MIT e da IBM Research \r\nconduzem pesquisas sobre IA e trabalham com organiza\u00e7\u00f5es globais \r\npara unir algoritmos ao seu impacto nos neg\u00f3cios e na sociedade. \r\nO\u00a0Notre Dame-IBM Tech Ethics Lab foi formado para abordar as diversas \r\nquest\u00f5es \u00e9ticas implicadas pelo desenvolvimento e uso de tecnologias \r\navan\u00e7adas, incluindo IA, aprendizado de m\u00e1quina (ML) e computa\u00e7\u00e3o \r\nqu\u00e2ntica. A pesquisa de Intelig\u00eancia Artificial Centrada no Homem (HAI) \r\nda Universidade de Stanford promove pesquisas, educa\u00e7\u00e3o, pol\u00edticas \r\ne\u00a0pr\u00e1ticas de IA.28 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\r\nContinue acompanhando este espa\u00e7o \r\npara obter mais informa\u00e7\u00f5es sobre os \r\n\u00faltimos avan\u00e7os em modelos de base \r\ne como a IBM est\u00e1 trabalhando para o \r\ndesenvolvimento respons\u00e1vel e uso desta \r\ne de outras tecnologias.\u00a9 Copyright IBM Corporation 2023, 2024\r\nIBM Brasil Ltda\r\nRua Tut\u00f3ia, 1157\r\nCEP 04007-900\r\nS\u00e3o Paulo, SP\r\nIBM Corporation\r\nNew Orchard Road\r\nArmonk, NY 10504\r\nProduzido nos \r\nEstados Unidos da Am\u00e9rica\r\nFevereiro de 2024\r\nIBM, o logotipo da IBM, Enterprise Design Thinking, IBM Consulting, IBM Research, \r\nIBM Watson, watsonx, watsonx.ai, watsonx.data e watsonx.governance s\u00e3o marcas \r\ncomerciais ou marcas registradas da International Business Machines Corporation, \r\nnos Estados Unidos e/ou em outros pa\u00edses. Outros nomes de produtos e servi\u00e7os \r\npodem ser marcas comerciais da IBM ou de outras empresas. Uma lista atual de \r\nmarcas comerciais da IBM est\u00e1 dispon\u00edvel em ibm.com/br-pt/trademark.\r\nEste documento \u00e9 atual na data de sua publica\u00e7\u00e3o inicial, podendo ser alterado \r\npela IBM a qualquer momento. Nem todas as ofertas est\u00e3o dispon\u00edveis em todos os \r\npa\u00edses nos quais a IBM opera. \r\nAS INFORMA\u00c7\u00d5ES CONTIDAS NESTE DOCUMENTO S\u00c3O FORNECIDAS NO ESTADO \r\nEM QUE SEM ENCONTRAM, SEM QUALQUER GARANTIA, EXPRESSA OU IMPL\u00cdCITA, \r\nINCLUSIVE SEM QUALQUER GARANTIA DE COMERCIALIZA\u00c7\u00c3O, ADEQUA\u00c7\u00c3O A \r\nDETERMINADO FIM E QUALQUER GARANTIA OU CONDI\u00c7\u00c3O DE N\u00c3O INFRA\u00c7\u00c3O. \r\nOs produtos IBM t\u00eam a garantia prevista nos termos e condi\u00e7\u00f5es dos contratos sob \r\nos quais s\u00e3o fornecidos.\r\nDeclara\u00e7\u00e3o de boas pr\u00e1ticas de seguran\u00e7a: nenhum sistema ou produto de TI deve \r\nser considerado completamente seguro, e nenhuma medida exclusiva de produto, \r\nservi\u00e7o ou seguran\u00e7a pode ser completamente eficaz na preven\u00e7\u00e3o de uso ou \r\nacesso inadequado. 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Suas principais preocupa\u00e7\u00f5es s\u00e3o \nciberseguran\u00e7a (57%), privacidade (51%) e precis\u00e3o (47%). Muitas \norganiza\u00e7\u00f5es estavam levando essas preocupa\u00e7\u00f5es a s\u00e9rio antes \nda \u2018consumeriza\u00e7\u00e3o\u2019 da IA generativa, expressando sua inten\u00e7\u00e3o de \ninvestir pelo menos 40% mais em \u00e9tica de IA nos pr\u00f3ximos tr\u00eas anos. \nA\u00a0conscientiza\u00e7\u00e3o sobre riscos e poss\u00edveis maneiras de mitig\u00e1-los \u00e9 o  \nprimeiro passo crucial para a cria\u00e7\u00e3o de sistemas de IA confi\u00e1veis.\nNeste documento:\nExploraremos as vantagens dos modelos de base, incluindo \nsua capacidade de realizar tarefas desafiadoras, potencial \npara acelerar a ado\u00e7\u00e3o de IA, habilidade de aumentar \na produtividade e os benef\u00edcios econ\u00f4micos que eles \nproporcionam. \nDiscutiremos as tr\u00eas categorias de risco, incluindo riscos \nconhecidos de formas anteriores de IA, riscos conhecidos \namplificados por modelos de base e riscos emergentes \nintr\u00ednsecos aos recursos generativos dos modelos de base. \nAbordaremos os princ\u00edpios, os pilares e o controle que \nformam a base das iniciativas \u00e9ticas de IA da IBM e \nsugeriremos barreiras para a mitiga\u00e7\u00e3o de riscos.\n5\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nIntrodu\u00e7\u00e3o\n\u00c0 medida que o uso de IA continua se expandindo, os grandes e \ncomplexos modelos de IA est\u00e3o fornecendo resultados promissores \nde desempenho, bem como resolvendo alguns dos problemas mais \ndesafiadores da sociedade. No entanto, criar grandes conjuntos de dados \nde treinamento e modelos complexos para cada aplicativo de IA pode \nser extremamente dif\u00edcil para as empresas. Modelos de base fornecem \num caminho para alcan\u00e7ar o melhor dos dois mundos: desenvolver \nmodelos de \u00faltima gera\u00e7\u00e3o poderosos e reutiliz\u00e1-los diretamente ou \naplicar m\u00e9todos de ajuste para implementar uma variedade de casos \nde uso, em vez de treinar novos modelos para cada caso de uso. Por \nexemplo, a IBM Research desenvolveu modelos de base para inspe\u00e7\u00e3o \nvisual. Esses modelos de base aprendem a representa\u00e7\u00e3o geral de \nsuperf\u00edcies e corredores de concreto e podem ser ajustados ainda \nmais para casos de uso espec\u00edficos, como detec\u00e7\u00e3o de rachaduras ou \ninspe\u00e7\u00e3o de defeitos com dados menos rotulados.\nA IBM define um modelo de base como um modelo de IA que pode \nser adaptado a uma ampla gama de tarefas de recebimento de dados. \nOs\u00a0modelos de base normalmente s\u00e3o modelos generativos de grande \nescala treinados em dados n\u00e3o rotulados usando autossupervis\u00e3o. \nComo\u00a0modelos de grande escala, os modelos de base podem incluir \nbilh\u00f5es de par\u00e2metros.\nA IBM \u00e9 uma empresa de nuvem h\u00edbrida e IA com vasta reputa\u00e7\u00e3o como \nadministradora de dados respons\u00e1vel e comprometida com a \u00e9tica em \nIA. Usando a capacidade de nossas equipes de pesquisa, produto e \nconsultoria, juntamente com parceiros externos, como a Hugging Face, \najudamos a trazer o poder dos modelos de base para nossos clientes \ne a criar IAs confi\u00e1veis em qualquer empresa. A IBM tamb\u00e9m continua \ninvestindo na cria\u00e7\u00e3o de novas plataformas, como a IA IBM watsonx \ne plataformas e tecnologias de dados, para projetar e desenvolver \nmodelos de IA para se comportar de maneira audit\u00e1vel e confi\u00e1vel. \nEste documento descreve o ponto de vista da IBM sobre a \u00e9tica dos \nmodelos de base. \u00c9 a primeira vers\u00e3o, e as vers\u00f5es futuras expandir\u00e3o \nv\u00e1rios aspectos da abordagem \u00e9tica do modelo de base da IBM. \nEsperamos que este documento seja \u00fatil para todos os stakeholders no \ndesenvolvimento, implementa\u00e7\u00e3o e uso do modelo de base de forma \nrespons\u00e1vel.\n6\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nBenef\u00edcios dos  \nmodelos de base\nOs modelos de base podem melhorar significativamente o processo de \ndesenvolvimento de sistemas de IA e auxiliar no avan\u00e7o da IA da fase de \nexplora\u00e7\u00e3o para a ado\u00e7\u00e3o nas empresas. Seus benef\u00edcios incluem:\nRealizar tarefas complexas \nModelos de base mostram um aumento significativo no desempenho \nna resolu\u00e7\u00e3o de problemas complexos e dif\u00edceis. Por exemplo, \no modelo de base geoespacial da colabora\u00e7\u00e3o IBM e NASA foi \nprojetado para converter os dados de sat\u00e9lite da NASA em mapas \nde desastres naturais, como inunda\u00e7\u00f5es e outras mudan\u00e7as de \ncen\u00e1rio. O modelo tamb\u00e9m pode ser usado para ajudar a revelar \no\u00a0passado do nosso planeta; estimar riscos para culturas, empresas \nou infraestruturas devido ao clima severo; desenvolver estrat\u00e9gias \npara se adaptar \u00e0s mudan\u00e7as clim\u00e1ticas; e auxiliar no agroneg\u00f3cio. \nO modelo est\u00e1 planejado para ser disponibilizado previamente aos \nclientes IBM por meio do IBM Environmental Intelligence Suite.\nPara ilustrar, o MoLFormer-XL da IBM \u00e9 um modelo de base que \n\u00e9\u00a0capaz de inferir a estrutura de mol\u00e9culas a partir de representa\u00e7\u00f5es \nsimples, tornando mais f\u00e1cil a aprendizagem de v\u00e1rias tarefas \nde recebimento de dados, como prever as propriedades f\u00edsicas \ne\u00a0qu\u00e2nticas de uma mol\u00e9cula, identificar mol\u00e9culas semelhantes, \nrastrear mol\u00e9culas j\u00e1 aprovadas para novos casos de uso e descobrir \nnovas mol\u00e9culas. Moderna e IBM est\u00e3o explorando formas de \nusar o MoLExer para ajudar a prever propriedades das mol\u00e9culas e \nentender as caracter\u00edsticas de poss\u00edveis medicamentos de mRNA.\nMaior produtividade \nA natureza generativa dos modelos de base amplia o n\u00famero de \n\u00e1reas em que a IA pode ser usada em uma empresa para ajudar a \nmelhorar a\u00a0produtividade, automatizando tarefas rotineiras e tediosas \ne\u00a0permitindo que os usu\u00e1rios dediquem mais tempo ao trabalho \ncriativo e inovador. Por exemplo, o IBM Watsonx Code Assistant, \ndesenvolvido com modelos de base, possibilita que desenvolvedores, \nindependentemente do n\u00edvel de experi\u00eancia, escrevam c\u00f3digos usando \nrecomenda\u00e7\u00f5es geradas por IA.\nTime to value mais r\u00e1pido \nModelos de base geralmente s\u00e3o treinados com dados n\u00e3o rotulados, \nque est\u00e3o mais dispon\u00edveis em grandes quantidades do que dados \nrotulados. Uma vez treinados, os modelos de base podem ser usados \ndiretamente ou ap\u00f3s serem ajustados para aplicativos de recebimento \nde dados, usando uma pequena quantidade de dados rotulados  \nespecializados, que podem diminuir a cria\u00e7\u00e3o do time to value.\n7\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nUtilize diversas modalidades de dados \nOs modelos de base podem ser treinados usando diversas modalidades \nde dados, como l\u00edngua natural, texto, imagem e \u00e1udio. Eles tamb\u00e9m \npodem ser aplicados a tarefas que exigem diferentes tipos de dados, \ncomo dados de s\u00e9ries temporais, dados geoespaciais, dados tabulares, \ndados semiestruturados e dados de modalidade mista, como texto \ncombinado com imagens.\nDespesas amortizadas \nEmbora o custo inicial do treinamento de um modelo de base seja \nsignificativamente maior do que o treinamento de um modelo de IA \ntradicional, o custo adicional para aplic\u00e1-lo em uma nova tarefa \u00e9 \nconsideravelmente menor. O uso de modelos de base pr\u00e9-treinados  \npoderia ajudar a eliminar a necessidade de que as empresas fa\u00e7am \ninvestimentos substanciais para treinar modelos de base e explorar suas \nnovas capacidades. Para uma empresa, a confiabilidade dos modelos, \na\u00a0efici\u00eancia energ\u00e9tica, o desempenho, a portabilidade e a capacidade \nde\u00a0usar dados corporativos de forma eficaz e segura s\u00e3o fundamentais.\nA IBM permite que as empresas \ncriem e detenham o valor de \nmodelos de base para seus \nneg\u00f3cios, trazendo as melhores \ninova\u00e7\u00f5es da comunidade de \nIA aberta e global, operando de \nforma eficiente em ambientes de \ncomputa\u00e7\u00e3o h\u00edbrida, ajudando \na mitigar riscos e controlando \nrigorosamente a IA.\n8\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nRiscos dos  \nmodelos de base\nComo todas as tecnologias que avan\u00e7am rapidamente, os modelos \nde base oferecem riscos e benef\u00edcios. Alguns s\u00e3o riscos legais, \ncomo restri\u00e7\u00f5es \u00e0 movimenta\u00e7\u00e3o ou uso de dados, e precisam \nser cuidadosamente avaliados de acordo com a legisla\u00e7\u00e3o atual e \nem evolu\u00e7\u00e3o. Outros riscos t\u00eam uma natureza \u00e9tica e devem ser \nconsiderados cuidadosamente para que a tecnologia tenha um impacto \npositivo. Em geral, os riscos de IA levantam quest\u00f5es sociot\u00e9cnicas \ne\u00a0devem ser abordados e mitigados por meio de m\u00e9todos sociot\u00e9cnicos, \nincluindo ferramentas de software, processos de avalia\u00e7\u00e3o de risco, \nframeworks de \u00e9tica em IA, mecanismos de controle, consultas \nmultistakeholder, padr\u00f5es e regulamenta\u00e7\u00e3o. Iremos listar os riscos \nconsiderando as seguintes 3 categorias:\n1.\t Tradicional. Riscos conhecidos de formas anteriores ou anteriores \nde\u00a0sistemas de IA\n2.\t Amplificados. Riscos conhecidos, mas agora intensificados devido \u00e0s \ncaracter\u00edsticas intr\u00ednsecas dos modelos de base, principalmente seus \nrecursos generativos inerentes\n3.\t Novo. Riscos emergentes intr\u00ednsecos aos modelos de base e suas \ncapacidades generativas inerentes\n \nTamb\u00e9m estruturamos a lista de riscos em rela\u00e7\u00e3o a se est\u00e3o \nprincipalmente associados ao conte\u00fado fornecido ao modelo \nbase, o\u00a0input, ou ao conte\u00fado gerado por ele, o output, ou se est\u00e3o \nrelacionados a desafios adicionais.\n9\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n1. Riscos associados \u00e0 entrada\nGrupo\nRisco\nIndicador\nFase de treinamento e ajuste\nJusti\u00e7a\nVi\u00e9s de dados: vi\u00e9s hist\u00f3rico, representacional \ne social presente nos dados usados para \ntreinar e fazer o ajuste fino do modelo.\nAmplificado\nTreinar um sistema de IA com dados enviesados, como vi\u00e9s hist\u00f3rico ou \nrepresentacional, pode resultar em outputs enviesados ou distorcidos \nque podem representar injustamente ou discriminar certos grupos \nou indiv\u00edduos. Al\u00e9m dos impactos negativos na sociedade, entidades \ncomerciais podem enfrentar consequ\u00eancias legais, interrup\u00e7\u00e3o \ndas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o decorrentes dos resultados \nenviesados do modelo.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nEnvenenamento de dados: um tipo de ataque \nadversarial no qual um advers\u00e1rio ou agente \ninterno malicioso injeta intencionalmente \namostras corrompidas, falsas, enganosas \nou incorretas no conjunto de dados de \ntreinamento ou ajuste fino.\nTradicional\nO envenenamento de dados pode tornar o modelo sens\u00edvel a um padr\u00e3o \nde dados malicioso e produzir o output desejado pelo advers\u00e1rio. Isso \npode criar um risco de seguran\u00e7a onde advers\u00e1rios podem manipular \no\u00a0comportamento do modelo em seu pr\u00f3prio benef\u00edcio.  Al\u00e9m de produzir \nresultados n\u00e3o intencionais e potencialmente maliciosos, uma diverg\u00eancia \ndo modelo causada por envenenamento de dados pode resultar em \nentidades comerciais enfrentando consequ\u00eancias legais, interrup\u00e7\u00e3o \ndas\u00a0opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\nRobustez\nCuradoria de dados: quando os dados de \ntreinamento ou ajuste s\u00e3o coletados ou \npreparados de forma inadequada.\nAmplificado\nUma curadoria de dados inadequada pode afetar adversamente como \num modelo \u00e9 treinado, resultando em um modelo que n\u00e3o se comporta \nde acordo com os valores pretendidos. Exemplos de uma curadoria de \ndados inadequada podem incluir erros de rotulagem ou anota\u00e7\u00e3o nos \ndados usados para treinar ou ajustar o modelo. Corrigir problemas ap\u00f3s \no treinamento e a implementa\u00e7\u00e3o do modelo pode ser insuficiente para \ngarantir um comportamento adequado. Um comportamento inadequado do \nmodelo pode resultar em entidades comerciais enfrentando consequ\u00eancias \nlegais, interrup\u00e7\u00f5es nas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\nAlinhamento \nde valor\nRetreinamento baseado em downstream: \nusando de outputs indesej\u00e1veis (imprecisos, \ninadequados, conte\u00fado do usu\u00e1rio, etc.) \nde aplica\u00e7\u00f5es downstream para fins de \nretreinamento.\nNovo\nO reaproveitamento de output downstream para treinar novamente um \nmodelo sem implementar a verifica\u00e7\u00e3o humana adequada aumenta as \nchances de que outputs indesej\u00e1veis sejam incorporados aos dados de \ntreinamento ou ajuste do modelo, possivelmente gerando outputs ainda \nmais indesej\u00e1veis.  Comportamento inadequado do modelo pode resultar \nem entidades empresariais enfrentando consequ\u00eancias legais ou danos \n\u00e0 reputa\u00e7\u00e3o.  N\u00e3o cumprir com as leis de transfer\u00eancia de dados pode \nresultar em multas e outras consequ\u00eancias legais.\nTransfer\u00eancia de dados: leis e outras \nrestri\u00e7\u00f5es podem limitar ou proibir a \ntransfer\u00eancia de dados.\nTradicional\nRestri\u00e7\u00f5es \u00e0 transfer\u00eancia de dados podem afetar a disponibilidade dos \ndados necess\u00e1rios para treinar um modelo de IA e podem resultar em \ndados mal representados. Al\u00e9m do impacto na disponibilidade de dados, \no n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de transfer\u00eancia de dados \npode resultar em multas e outras consequ\u00eancias legais. \nLeis de dados\nUso de dados: leis e outras restri\u00e7\u00f5es podem \nlimitar ou proibir o uso de alguns dados para \ncasos de uso espec\u00edficos de IA.\nTradicional\nO n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de uso de dados pode \nresultar em multas e outras consequ\u00eancias legais. \nAquisi\u00e7\u00e3o de dados: leis e outras \nregulamenta\u00e7\u00f5es podem limitar a coleta \nde certos tipos de dados para casos de uso \nespec\u00edficos de IA.\nAmplificado\nO n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es da aquisi\u00e7\u00e3o de dados \npode resultar em multas e outras consequ\u00eancias legais. \n10\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nGrupo\nRisco\nIndicador\nPropriedade \nintelectual\nDireitos de uso de dados: termos de servi\u00e7o, \nleis de direitos autorais, conformidade com \nlicen\u00e7as ou outras quest\u00f5es de propriedade \nintelectual podem restringir a capacidade \nde usar certos dados para a constru\u00e7\u00e3o \nde\u00a0modelos. \nAmplificado\nAs leis e regulamenta\u00e7\u00f5es referentes ao uso de dados para treinar IA \ns\u00e3o inst\u00e1veis e podem variar de pa\u00eds para pa\u00eds, o que cria desafios no \ndesenvolvimento de modelos. Se o uso de dados violar regras ou restri\u00e7\u00f5es, \nas entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nTranspar\u00eancia de dados: desafio em \ndocumentar como os dados de um modelo \nforam coletados, curados e utilizados \npara\u00a0trein\u00e1-lo.\nAmplificado\nA transpar\u00eancia dos dados \u00e9 importante para a conformidade legal e \u00e9tica \nda IA. A falta de informa\u00e7\u00f5es limita a capacidade de avaliar os riscos \nassociados aos dados. A falta de requisitos padronizados pode limitar a \ndivulga\u00e7\u00e3o, pois as organiza\u00e7\u00f5es protegem segredos comerciais e tentam \nevitar que outros copiem seus modelos.\nTranspar\u00eancia\nProced\u00eancia dos dados: desafio em \npadronizar e estabelecer m\u00e9todos para \nverificar de onde os dados vieram.\nAmplificado\nNem todas as fontes de dados s\u00e3o confi\u00e1veis. Os dados podem ter sido \ncoletados, manipulados ou falsificados de forma anti\u00e9tica. O uso de dados \nn\u00e3o confi\u00e1veis pode resultar em comportamentos indesej\u00e1veis no modelo. \nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nInforma\u00e7\u00f5es pessoais nos dados: inclus\u00e3o \nou presen\u00e7a de informa\u00e7\u00f5es pessoalmente \nidentific\u00e1veis (PII) e informa\u00e7\u00f5es pessoais \nsens\u00edveis (SPI) nos dados usados para treinar \nou ajustar o modelo.\nTradicional\nSe n\u00e3o desenvolvido adequadamente para proteger dados sens\u00edveis, \no\u00a0modelo pode expor informa\u00e7\u00f5es pessoais no output gerado. Al\u00e9m disso, \ndados pessoais ou sens\u00edveis devem ser revisados e tratados de acordo \ncom as leis e regulamenta\u00e7\u00f5es de privacidade. As entidades empresariais \npodem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es \ne\u00a0outras consequ\u00eancias legais se forem encontradas em viola\u00e7\u00e3o.\nPrivacidade\nReidentifica\u00e7\u00e3o: mesmo com a remo\u00e7\u00e3o de \ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis \n(PII) e informa\u00e7\u00f5es pessoais sens\u00edveis (SPI) \ndos dados, ainda pode ser poss\u00edvel identificar \npessoas devido a outros recursos dispon\u00edveis \nnos dados. \nTradicional\nOs dados que podem revelar informa\u00e7\u00f5es pessoais ou sens\u00edveis devem \nser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es de privacidade, pois \nas entidades comerciais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se forem \nconsideradas em viola\u00e7\u00e3o.\nDireitos de privacidade de dados: desafios \nrelacionados \u00e0 capacidade de fornecer \ndireitos do titular dos dados, como op\u00e7\u00e3o \nde exclus\u00e3o, direito de acesso e direito ao \nesquecimento.\nAmplificado\nA identifica\u00e7\u00e3o ou uso inadequado de dados pode resultar em viola\u00e7\u00e3o das \nleis de privacidade. O uso inadequado ou um pedido de remo\u00e7\u00e3o de dados \npoderia obrigar as organiza\u00e7\u00f5es a reconfigurar o modelo, o que \u00e9 caro. \nAl\u00e9m disso, as entidades empresariais podem enfrentar multas, danos \u00e0 \nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se n\u00e3o \ncumprirem as regras e regulamenta\u00e7\u00f5es de privacidade de dados.\nConsentimento informado: dados \ncoletados para treinar modelos de IA sem \no consentimento informado do propriet\u00e1rio, \nmesmo quando legalmente permitido.\nTradicional\nEm algumas circunst\u00e2ncias, pode ser anti\u00e9tico coletar e usar dados \nsem o\u00a0consentimento da pessoa. Existem tamb\u00e9m poss\u00edveis riscos \nreputacionais associados a esse tipo de uso.\n11\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nGrupo\nRisco\nIndicador\nInfer\u00eancia Fase\nPrivacidade\nInforma\u00e7\u00f5es pessoais no prompt: divulgar \ninforma\u00e7\u00f5es pessoais ou informa\u00e7\u00f5es \npessoais sens\u00edveis como parte do prompt \nsolicita\u00e7\u00e3o enviada ao modelo.\nNovo\nOs dados do prompt podem ser armazenados ou posteriormente utilizados \npara outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos \nde dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es \nde privacidade. Sem um armazenamento e uso adequados dos dados, \nas entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nInforma\u00e7\u00f5es de IP no prompt: divulga\u00e7\u00e3o de \ninforma\u00e7\u00f5es de direitos autorais ou outras \ninforma\u00e7\u00f5es de propriedade intelectual como \nparte do prompt enviado ao modelo.\nNovo\nOs dados do prompt podem ser armazenados ou posteriormente utilizados \npara outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos \nde dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es \nde propriedade intelectual. Sem um armazenamento e uso adequados \ndos dados, as entidades empresariais podem enfrentar multas, danos \n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPropriedade \nintelectual\nDados confidenciais no prompt: inclus\u00e3o de \ndados confidenciais como parte do prompt \nenviado ao modelo.\nNovo\nSe n\u00e3o for desenvolvido adequadamente para proteger dados confidenciais, \no modelo pode expor informa\u00e7\u00f5es confidenciais ou propriedade intelectual \nno output gerado. Al\u00e9m disso, informa\u00e7\u00f5es confidenciais dos usu\u00e1rios finais \npodem ser coletadas e armazenadas inadvertidamente.\nRobustez\nAtaque de evas\u00e3o: tentativa de fazer com \nque um modelo produza outputs incorretos \nperturbando os dados enviados ao modelo \ntreinado.\nAmplificado\nOs ataques de evas\u00e3o alteram o comportamento do modelo, geralmente \npara beneficiar o atacante. Se os resultados de output n\u00e3o forem \ndevidamente considerados, as entidades empresariais podem enfrentar \nmultas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \nconsequ\u00eancias legais.\nAtaques baseados em prompt: ataques \nadversos, como inje\u00e7\u00e3o de prompt (tentativa \nde for\u00e7ar um modelo a produzir um output \ninesperado), vazamento de prompt (tentativas \nde extrair o prompt do sistema de um \nmodelo), desbloqueio (tentativas de romper \nas prote\u00e7\u00f5es estabelecidas no modelo), \ne\u00a0prepara\u00e7\u00e3o de prompt (tentativa de for\u00e7ar \num modelo a produzir um output alinhado \nao\u00a0prompt).\nNovo\nDependendo do conte\u00fado revelado, as entidades empresariais podem \nenfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \nconsequ\u00eancias legais.\n12\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n2. Riscos associados \u00e0 sa\u00edda\nGrupo\nRisco\nIndicador\nJusti\u00e7a\nVi\u00e9s de output: o conte\u00fado gerado pode \nrepresentar injustamente certos grupos ou \nindiv\u00edduos.\nNovo\nO vi\u00e9s pode prejudicar os usu\u00e1rios dos modelos de IA e amplificar \ncomportamentos discriminat\u00f3rios existentes. As entidades empresariais \npodem enfrentar danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras \nconsequ\u00eancias.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nVi\u00e9s de decis\u00e3o: quando um grupo \u00e9 \ninjustamente favorecido em rela\u00e7\u00e3o a outro \ndevido aos efeitos das decis\u00f5es tomadas por \nhumanos usando o output do modelo.\nTradicional\nO vi\u00e9s pode prejudicar as pessoas afetadas pelas decis\u00f5es do modelo. \nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nViola\u00e7\u00e3o de direitos autorais: quando \num modelo gera conte\u00fado que \u00e9 muito \nsemelhante ou id\u00eantico a uma obra existente \nprotegida por direitos autorais ou abrangida \npor um acordo de licen\u00e7a de c\u00f3digo aberto.\nNovo\nAs leis e regulamenta\u00e7\u00f5es referentes ao uso de conte\u00fado que se assemelha \nou \u00e9 muito semelhante a outros dados protegidos por direitos autorais s\u00e3o \namplamente indefinidos e podem variar de pa\u00eds para pa\u00eds, o que representa \ndesafios na determina\u00e7\u00e3o e implementa\u00e7\u00e3o da conformidade. As entidades \nempresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das \nopera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPropriedade \nintelectual\nAlucina\u00e7\u00e3o: gera\u00e7\u00e3o de conte\u00fado \nfactualmente impreciso ou n\u00e3o verdadeiro.\nNovo\nOutputs falsos podem induzir os usu\u00e1rios ao erro e serem incorporados \nem artefatos posteriores, propagando ainda mais a desinforma\u00e7\u00e3o. Isso \npode prejudicar tanto os propriet\u00e1rios quanto os usu\u00e1rios dos modelos de \nIA. Tamb\u00e9m, as entidades empresariais podem enfrentar multas, danos \n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nOutputs t\u00f3xicos: quando o modelo produz \nconte\u00fado odioso, abusivo e profano (HAP) ou \nobsceno.\nNovo\nConte\u00fado odioso, abusivo e profano (HAP) ou obsceno pode impactar \nadversamente e prejudicar as pessoas que interagem com o modelo. \nTamb\u00e9m, as entidades empresariais podem enfrentar multas, danos \u00e0 \nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nAlinhamento de \nvalor\nConselhos perigosos: quando um modelo \nfornece conselhos sem ter informa\u00e7\u00f5es \nsuficientes, resultando em poss\u00edveis perigos \nse o conselho for seguido.\nNovo\nUma pessoa pode agir com base em conselhos incompletos ou \npreocupar-se com uma situa\u00e7\u00e3o que n\u00e3o se aplica a ela devido \u00e0 natureza \nsupergeneralizada do conte\u00fado gerado.\nDissemina\u00e7\u00e3o de desinforma\u00e7\u00e3o: utiliza\u00e7\u00e3o \nde um modelo para criar informa\u00e7\u00f5es \nenganosas ou falsas com o intuito de enganar \nou influenciar um p\u00fablico-alvo.\nNovo\nEspalhar desinforma\u00e7\u00e3o pode afetar a capacidade de uma pessoa \nde tomar decis\u00f5es informadas. As entidades empresariais podem \nenfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es \ne\u00a0outras consequ\u00eancias\u00a0legais.\nToxicidade: utilizar um modelo para gerar \nconte\u00fado odioso, abusivo e profano (HAP) \nou\u00a0obsceno.\nNovo\nConte\u00fado t\u00f3xico pode ter um impacto negativo no bem-estar de seus \ndestinat\u00e1rios. As entidades empresariais podem enfrentar multas, danos \n\u00e0\u00a0reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nUso indevido \nUso n\u00e3o consensual: utilizar um modelo para \nimitar pessoas por meio de v\u00eddeo (deepfakes), \nimagens, \u00e1udio ou outras modalidades sem \no\u00a0consentimento delas.\nAmplificado\nDeepfakes podem disseminar desinforma\u00e7\u00e3o sobre uma pessoa, \npossivelmente resultando em impactos negativos na reputa\u00e7\u00e3o da pessoa. \nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nExpor informa\u00e7\u00f5es pessoais: quando \ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis (PII) \nou informa\u00e7\u00f5es pessoais sens\u00edveis (SPI) s\u00e3o \nutilizadas nos dados de treinamento, dados \nde ajuste fino ou como parte do prompt, \nos modelos podem revelar esses dados no \noutput gerado.\nNovo\nCompartilhar informa\u00e7\u00f5es pessoalmente identific\u00e1veis das pessoas afeta \nseus direitos e as torna mais vulner\u00e1veis. Al\u00e9m disso, os dados dos outputs \ndevem ser revisados em conformidade com as leis e regulamenta\u00e7\u00f5es de \nprivacidade, pois as entidades comerciais podem enfrentar multas, danos \n\u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se \nforem encontradas em viola\u00e7\u00e3o das leis ou regulamenta\u00e7\u00f5es de\u00a0privacidade \nou uso de dados. \nPrivacidade\nOutput inexplic\u00e1vel: desafios em explicar por \nque o output do modelo foi gerado.\nAmplificado \nOs modelos de base s\u00e3o baseados em arquiteturas complexas de \ndeep learning, tornando as explica\u00e7\u00f5es para seus outputs dif\u00edceis. \nSem\u00a0explica\u00e7\u00f5es claras para o output do modelo, \u00e9 dif\u00edcil para os usu\u00e1rios, \nvalidadores do modelo e auditores entenderem e confiarem no modelo. \nA\u00a0falta de transpar\u00eancia pode acarretar consequ\u00eancias legais em dom\u00ednios \naltamente regulamentados. Explica\u00e7\u00f5es equivocadas podem levar a uma \nconfian\u00e7a excessiva.\nExplicabilidade\nAtribui\u00e7\u00e3o n\u00e3o confi\u00e1vel de fontes: \ndesafios em determinar de quais dados de \ntreinamento ou ajuste fino o modelo gerou \numa parte ou todo o seu output.\nNovo\nA incapacidade de rastrear a origem ou proced\u00eancia da sa\u00edda torna \ndif\u00edcil para os usu\u00e1rios, validadores de modelo e auditores entenderem \ne\u00a0confiarem no modelo.\nRastreabilidade\nExcesso/falta de confian\u00e7a: quando uma \npessoa deposita confian\u00e7a em excesso ou em \nfalta na orienta\u00e7\u00e3o de um modelo de IA.\nAmplificado\nEm tarefas onde os humanos baseiam suas escolhas em sugest\u00f5es da IA, \numa confian\u00e7a excessiva ou insuficiente pode levar a decis\u00f5es inadequadas \ndevido \u00e0 confian\u00e7a equivocada no sistema de IA, com consequ\u00eancias \nnegativas que aumentam com a import\u00e2ncia da decis\u00e3o. Decis\u00f5es ruins \npodem prejudicar as pessoas e podem resultar em preju\u00edzos financeiros, \ndanos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias \nlegais para as entidades comerciais.\nConfian\u00e7a \nequivocada\n13\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nGrupo\nRisco\nIndicador\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nUso perigoso: utilizar um modelo com a \u00fanica \ninten\u00e7\u00e3o de prejudicar pessoas.\nNovo\nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nUso inadequado: utilizar um modelo para um \nfim para o qual o modelo n\u00e3o foi projetado.\nAmplificado\nReutilizar um modelo sem compreender seus dados originais, inten\u00e7\u00e3o \nde design e objetivos pode resultar em comportamentos inesperados \ne\u00a0indesejados do modelo.\nGera\u00e7\u00e3o de c\u00f3digo prejudicial: modelos \npodem gerar c\u00f3digo que, quando executado, \ncausa danos ou afeta inadvertidamente \noutros sistemas.\nNovo\nA execu\u00e7\u00e3o de c\u00f3digo prejudicial pode abrir vulnerabilidades nos sistemas \nde TI. As entidades empresariais podem enfrentar multas, danos \u00e0 \nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nGera\u00e7\u00e3o \nde c\u00f3digo \nprejudicial\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o revelar que o conte\u00fado \n\u00e9\u00a0gerado por um modelo de IA.\nNovo\nA omiss\u00e3o do conte\u00fado produzido por IA pode ser interpretada como \nenganosa, levando a uma diminui\u00e7\u00e3o da confian\u00e7a. A inten\u00e7\u00e3o de enganar \npode resultar na redu\u00e7\u00e3o da capacidade de a\u00e7\u00e3o humana, em multas, \ndanos \u00e0 reputa\u00e7\u00e3o e outras consequ\u00eancias legais.\n14\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n3. Desafios\nGrupo\nRisco\nIndicador\nControle\nTranspar\u00eancia do Modelo: a falta de \ntranspar\u00eancia do modelo ou documenta\u00e7\u00e3o \ninsuficiente do processo de desenvolvimento \ndo modelo dificulta a compreens\u00e3o de como \ne por que um modelo foi constru\u00eddo e quem o \nconstruiu, aumentando assim a possibilidade \nde uso n\u00e3o intencional do modelo.\nTradicional\nA transpar\u00eancia \u00e9 importante para conformidade legal, \u00e9tica em IA e \norienta\u00e7\u00e3o para o uso apropriado de modelos. A falta de informa\u00e7\u00f5es \npode tornar mais dif\u00edcil avaliar os riscos, alterar o modelo ou reutiliz\u00e1-lo. \nO conhecimento sobre quem construiu um modelo tamb\u00e9m pode ser um \nfator importante na decis\u00e3o de confiar nele.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nResponsabilidade: o processo de \ndesenvolvimento de modelos de base \u00e9 \ncomplexo, com muitos dados, processos \ne pap\u00e9is envolvidos. Quando o output do \nmodelo n\u00e3o funciona conforme o esperado, \npode ser dif\u00edcil determinar a causa raiz e \natribuir responsabilidade. \nAmplificado\nSem documentar adequadamente decis\u00f5es e atribuir responsabilidades, \npode n\u00e3o ser poss\u00edvel determinar a responsabilidade por comportamentos \ninesperados ou uso indevido.\nResponsabilidade legal: Determinar quem \n\u00e9\u00a0respons\u00e1vel pelo modelo de base.\nNovo\nSe a propriedade ou responsabilidade pelo desenvolvimento do modelo for \nincerta, reguladores e outras partes interessadas podem ter preocupa\u00e7\u00f5es \nem rela\u00e7\u00e3o ao modelo, porque n\u00e3o ficar\u00e1 claro quem \u00e9, ou deveria ser, \nrespons\u00e1vel por problemas com ele ou pode responder a perguntas sobre \nele. Usu\u00e1rios de modelos sem propriedade clara podem enfrentar desafios \npara cumprir futuras regulamenta\u00e7\u00f5es de IA.\nConformidade \nlegal\nPropriedade do Conte\u00fado Gerado: determinar \na propriedade do conte\u00fado gerado por IA.\nNovo\nAs leis e regulamenta\u00e7\u00f5es relacionadas \u00e0 propriedade do conte\u00fado gerado \npor IA est\u00e3o em grande parte indefinidas e podem variar de pa\u00eds para \npa\u00eds. Entidades empresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, \ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\nPropriedade Intelectual do Conte\u00fado \nGerado: incerteza legal sobre os direitos \nde propriedade intelectual relacionados ao \nconte\u00fado gerado.\nNovo\nAs leis e regulamenta\u00e7\u00f5es sobre a determina\u00e7\u00e3o da possibilidade de \ndireitos autorais e da patenteabilidade do conte\u00fado gerado por IA est\u00e3o \nem grande parte indefinidas e podem variar de pa\u00eds para pa\u00eds. Entidades \nempresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das \nopera\u00e7\u00f5es e outras consequ\u00eancias legais se o conte\u00fado gerado estiver \nprotegido por direitos de propriedade intelectual.\nAtribui\u00e7\u00e3o da Fonte: determinar a \nproced\u00eancia do conte\u00fado gerado.\nAmplificado\nSe o modelo gera um output que \u00e9 id\u00eantico aos dados usados para \ntreinar o modelo, ele deve fornecer a proveni\u00eancia desse output. A falha \nem fazer isso pode colocar as entidades comerciais que implementam \nou usam o modelo em risco legal.\nImpacto nos Empregos: a ado\u00e7\u00e3o \ngeneralizada de sistemas de IA baseados em \nmodelos fundamentais pode levar \u00e0 perda \nde empregos das pessoas, \u00e0 medida que seu \ntrabalho \u00e9 automatizado, se elas n\u00e3o forem \ncapacitadas para novas habilidades. \nAmplificado\nA perda de empregos pode levar a uma redu\u00e7\u00e3o de renda e, portanto, \npode ter um impacto negativo na sociedade e no bem-estar humano. \nO ressurgimento pode ser desafiador dada a velocidade da evolu\u00e7\u00e3o \ntecnol\u00f3gica. \nSocial \nImpacto\nExplora\u00e7\u00e3o Humana: uso de trabalho \nfantasma (ghost work) na forma\u00e7\u00e3o de \nmodelos de IA, condi\u00e7\u00f5es de trabalho \ninadequadas, falta de cuidados de sa\u00fade, \nincluindo sa\u00fade mental, compensa\u00e7\u00e3o \ninjusta.\nAmplificado\nOs modelos de base ainda dependem do trabalho humano para obter, \ngerenciar e engenhar os dados que s\u00e3o usados para treinar o modelo. \nA\u00a0explora\u00e7\u00e3o humana para essas atividades pode ter um impacto negativo \nna sociedade e no bem-estar humano. Al\u00e9m disso, entidades empresariais \npodem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e \noutras consequ\u00eancias legais.\n15\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nGrupo\nRisco\nIndicador\nImpacto na Diversidade Cultural: \nos\u00a0sistemas de IA podem representar \nexcessivamente certas culturas, resultando \nna homogeneiza\u00e7\u00e3o da cultura e dos \npensamentos.\nNovo\nAs l\u00ednguas, pontos de vista e institui\u00e7\u00f5es de grupos sub-representados \npodem ser suprimidos, reduzindo assim a diversidade de pensamento \ne\u00a0cultura.\nPor que isso \u00e9 uma preocupa\u00e7\u00e3o?\nImpacto na Atua\u00e7\u00e3o Humana: desinforma\u00e7\u00e3o \ne manipula\u00e7\u00e3o geradas por modelos de base, \nincluindo a gera\u00e7\u00e3o de conte\u00fado manipulador.\nAmplificado\nA IA pode gerar desinforma\u00e7\u00e3o que parece real. Portanto, as pessoas \npodem n\u00e3o reconhec\u00ea-la como informa\u00e7\u00e3o falsa. Al\u00e9m disso, pode facilitar \na capacidade de agentes mal intencionados gerarem conte\u00fado com a \ninten\u00e7\u00e3o de manipular os pensamentos e o comportamento humano. \nImpacto na Educa\u00e7\u00e3o \u2013 Contornando o \nAprendizado: utiliza\u00e7\u00e3o de modelos de IA \npara contornar o processo de aprendizado.\nNovo\nOs modelos de IA facilitam a r\u00e1pida localiza\u00e7\u00e3o de solu\u00e7\u00f5es ou \nresolu\u00e7\u00e3o de problemas complexos. Esses sistemas podem ser usados \nindevidamente por estudantes para contornar o processo de aprendizado. \nA facilidade de acesso a esses modelos resulta em estudantes com uma \ncompreens\u00e3o superficial dos conceitos e dificulta a educa\u00e7\u00e3o adicional que \npode depender do entendimento desses conceitos.\nImpacto na Educa\u00e7\u00e3o \u2013 Pl\u00e1gio: utiliza\u00e7\u00e3o de \nmodelos de IA para plagiar intencional ou \ninadvertidamente trabalhos existentes.\nNovo\nOs modelos de IA podem ser usados para reivindicar a autoria ou \noriginalidade de trabalhos que foram criados por outras pessoas, \nenvolvendo-se assim em pl\u00e1gio. Reivindicar o trabalho de outras pessoas \ncomo pr\u00f3prio \u00e9 tanto anti\u00e9tico quanto frequentemente ilegal.\nImpacto no Meio Ambiente: aumento das \nemiss\u00f5es de carbono e do uso de \u00e1gua para \ntreinar e operar modelos de IA.\nAmplificado\nO consumo de grandes quantidades de energia para o treinamento de IA \ncontribui para as emiss\u00f5es de carbono que podem acelerar as mudan\u00e7as \nclim\u00e1ticas. Os recursos h\u00eddricos utilizados para resfriar os servidores \nde data center de IA n\u00e3o podem mais ser alocados para outros usos \nnecess\u00e1rios.\n16\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nExemplos de risco: Input\nRisco\nExemplo\nVi\u00e9s de dados: vi\u00e9s hist\u00f3rico, \nrepresentacional e social \npresente nos dados usados \npara treinar e fazer o ajuste \nfino do modelo.\nVi\u00e9s no setor de sa\u00fade\nPesquisas sobre o refor\u00e7o das disparidades na medicina destacam que o uso de dados e IA para transformar a \nforma como as pessoas recebem assist\u00eancia m\u00e9dica \u00e9 t\u00e3o eficaz quanto os dados que o sustentam. Isso significa \nque o uso de dados de treinamento com pouca representa\u00e7\u00e3o de minorias ou que reflete cuidados j\u00e1 desiguais \npode aumentar as desigualdades em sa\u00fade.   \n[Forbes, Dezembro de 2022]\nRetreinamento baseado \nem downstream: usando \nde outputs indesej\u00e1veis \n(imprecisos, inadequados, \nconte\u00fado do usu\u00e1rio, etc.) \nde aplica\u00e7\u00f5es downstream \npara fins de retreinamento\nColapso do modelo devido ao treinamento usando conte\u00fado gerado por IA\nConforme afirmado no artigo de origem, um grupo de pesquisadores investigou o problema de utilizar conte\u00fado \ngerado por IA para treinamento em vez de conte\u00fado gerado por humanos. Eles descobriram que os grandes \nmodelos de linguagem por tr\u00e1s da tecnologia podem potencialmente ser treinados em outros conte\u00fados gerados \npor IA, \u00e0 medida que continuam a se espalhar em grande escala pela internet, um fen\u00f4meno que cunharam como \n\u201ccolapso do modelo\u201d.\n[Business Insider, agosto de 2023]\nTransfer\u00eancia de dados: \nleis e outras restri\u00e7\u00f5es \npodem limitar ou proibir \na\u00a0transfer\u00eancia de dados.\nLeis de restri\u00e7\u00e3o de dados\nConforme afirmado no artigo de pesquisa, medidas de localiza\u00e7\u00e3o de dados que restringem a capacidade de \nmigrar dados globalmente reduzir\u00e3o a capacidade de desenvolver capacidades de IA personalizadas. Isso afetar\u00e1 \na IA diretamente, fornecendo menos dados de treinamento e indiretamente, minando os blocos de constru\u00e7\u00e3o \nsobre os quais a IA \u00e9 constru\u00edda. \nExemplos incluem as restri\u00e7\u00f5es do GDPR sobre o processamento e uso de dados pessoais.\n[Brookings, dezembro de 2018] \nDireitos de uso de dados: \ntermos de servi\u00e7o, leis \nde direitos autorais, \nconformidade com licen\u00e7as \nou outras quest\u00f5es de \npropriedade intelectual \npodem restringir a \ncapacidade de usar certos \ndados para a constru\u00e7\u00e3o de \nmodelos. \nReivindica\u00e7\u00f5es de viola\u00e7\u00e3o de direitos autorais de texto\nConforme declarado no artigo de origem, The New York Times processou a OpenAI e a Microsoft, acusando-as \nde usar milh\u00f5es de artigos do jornal sem permiss\u00e3o para ajudar a treinar chatbots a fornecer informa\u00e7\u00f5es \naos\u00a0leitores.\n[Reuters, dezembro de 2023]\nTreinamento e ajuste Fase\nGrupo\nJusti\u00e7a\nAlinhamento \nde valor\nLeis de dados\nPropriedade \nintelectual\nExemplos de risco\nN\u00f3s fornecemos exemplos cobertos pela imprensa para ajudar \na\u00a0explicar muitos dos riscos dos modelos de base.\u00a0Muitos desses \neventos cobertos pela imprensa ainda est\u00e3o em evolu\u00e7\u00e3o ou foram \nresolvidos, e fazer refer\u00eancia a eles pode ajudar o leitor a entender os \nriscos potenciais e trabalhar para mitig\u00e1-los.\u00a0Destacar esses exemplos \n\u00e9\u00a0apenas para fins ilustrativos.\u00a0\nA\u00e7\u00e3o Judicial Sobre LLM Unlearning\nDe acordo com o relat\u00f3rio, foi movida uma a\u00e7\u00e3o judicial contra o Google que alega o uso de material protegido \npor direitos autorais e informa\u00e7\u00f5es pessoais como dados de treinamento para seus sistemas de IA, incluindo \nseu chatbot Bard. Os direitos de optar por n\u00e3o participar e exclus\u00e3o s\u00e3o garantidos para os residentes da \nCalif\u00f3rnia conforme a CCPA e para crian\u00e7as nos Estados Unidos com menos de 13 anos conforme a COPPA. \nOs\u00a0autores alegam que, porque n\u00e3o h\u00e1 maneira para o Bard \u201cdesaprender\u201d ou remover completamente todas as \ninforma\u00e7\u00f5es pessoais coletadas que ele recebeu. Os autores observam que o aviso de privacidade do Bard afirma \nque as conversas do Bard n\u00e3o podem ser exclu\u00eddas pelo usu\u00e1rio depois de terem sido revisadas e anotadas \npela empresa e podem ser mantidas por at\u00e9 3 anos, o que os autores alegam contribuir ainda mais para a n\u00e3o \nconformidade com essas leis. \n[Reuters, julho de 2023] [J.L. v. Alphabet Inc.]\n17\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nRisco\nExemplo\nInforma\u00e7\u00f5es pessoais \nnos dados: inclus\u00e3o ou \npresen\u00e7a de informa\u00e7\u00f5es \npessoalmente \nidentific\u00e1veis (PII) e \ninforma\u00e7\u00f5es pessoais \nsens\u00edveis (SPI) nos dados \nusados para treinar ou \najustar o modelo.\nTreinamento sobre informa\u00e7\u00f5es privadas\nDe acordo com o artigo, o Google e sua empresa controladora, Alphabet, foram acusados em uma a\u00e7\u00e3o coletiva \nde usar uma vasta quantidade de informa\u00e7\u00f5es pessoais e material protegido por direitos autorais retirados do \nque \u00e9 descrito como centenas de milh\u00f5es de usu\u00e1rios da internet para treinar seus produtos de intelig\u00eancia \nartificial comercial, que inclui o Bard, seu chatbot de intelig\u00eancia artificial conversacional. \n[Reuters, julho de 2023] [J.L. v. Alphabet Inc.]\nGrupo\nPrivacidade\nDireitos de privacidade \nde dados: desafios \nrelacionados \u00e0 capacidade \nde fornecer direitos do \ntitular dos dados, como \nop\u00e7\u00e3o de exclus\u00e3o, direito \nde acesso e direito ao \nesquecimento.\nDireito de ser esquecido (RTBF)\nAs leis em v\u00e1rias localidades, incluindo a Europa (GDPR), concedem aos titulares de dados o direito de solicitar \nque dados pessoais sejam deletados por organiza\u00e7\u00f5es (\u2018Direito ao Esquecimento\u2019, ou RTBF). No entanto, \nos\u00a0sistemas de software habilitados por modelos de linguagem de grande escala (LLM) emergentes e cada vez \nmais populares apresentam novos desafios para esse direito. De acordo com uma pesquisa do Data61 da CSIRO, \nos\u00a0titulares de dados s\u00f3 podem identificar o uso de suas informa\u00e7\u00f5es pessoais em um LLM \u201cou inspecionando \no conjunto de dados de treinamento original ou talvez por enviar prompts do modelo\u201d. No entanto, os dados \nde treinamento podem n\u00e3o ser p\u00fablicos, ou as empresas optam por n\u00e3o divulg\u00e1-los, citando preocupa\u00e7\u00f5es \ncom seguran\u00e7a e outros motivos. As prote\u00e7\u00f5es tamb\u00e9m podem evitar que os usu\u00e1rios acessem as informa\u00e7\u00f5es \natrav\u00e9s de prompts. \n[Zhang et al.]\nTranspar\u00eancia de dados: \ndesafio em documentar \ncomo os dados de um \nmodelo foram coletados, \ncurados e utilizados \npara\u00a0trein\u00e1-lo.\nDivulga\u00e7\u00e3o de metadados de dados e modelos\nO relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do \nmodelo.  Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os \nconsumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de \nuso indevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, os autores afirmam: \u201cdado tanto o cen\u00e1rio competitivo \nquanto as implica\u00e7\u00f5es de seguran\u00e7a dos modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m \nmais detalhes sobre a arquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento, \nconstru\u00e7\u00e3o do conjunto de dados, m\u00e9todo de treinamento, ou similar.\u201d\n[OpenAI, mar\u00e7o de 2023]\nTranspar\u00eancia\n18\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nRisco\nExemplo\nInforma\u00e7\u00f5es pessoais \nno prompt: divulgar \ninforma\u00e7\u00f5es pessoais ou \ninforma\u00e7\u00f5es pessoais \nsens\u00edveis como parte \ndo prompt solicita\u00e7\u00e3o \nenviada ao modelo.\nDivulgar informa\u00e7\u00f5es pessoais de sa\u00fade em prompts do ChatGPT\nConforme os artigos de origem, algumas pessoas utilizam chatbots de IA para apoiar sua sa\u00fade mental. \nOs\u00a0usu\u00e1rios podem ter tend\u00eancia a incluir informa\u00e7\u00f5es pessoais de sa\u00fade em suas solicita\u00e7\u00f5es durante \na\u00a0intera\u00e7\u00e3o, o que poderia suscitar preocupa\u00e7\u00f5es com privacidade.\n[Time, outubro de 2023] [Forbes, abril de 2023]\nDados confidenciais \nno prompt: inclus\u00e3o de \ndados confidenciais como \nparte do prompt enviado \nao modelo.\nDivulga\u00e7\u00e3o de informa\u00e7\u00f5es confidenciais\nConforme o artigo de origem, um funcion\u00e1rio da Samsung acidentalmente vazou c\u00f3digo-fonte interno sens\u00edvel \npara o ChatGPT.\n[Forbes, maio de 2023] \nInfer\u00eancia Fase\nGrupo\nPropriedade \nintelectual\nRobustez\nPrivacidade\nAtaques baseados \nem prompt: ataques \nadversos, como inje\u00e7\u00e3o \nde prompt (tentativa \nde for\u00e7ar um modelo \na produzir um output \ninesperado), vazamento \nde prompt (tentativas \nde extrair o prompt do \nsistema de um modelo), \ndesbloqueio (tentativas \nde romper as prote\u00e7\u00f5es \nestabelecidas no \nmodelo), e prepara\u00e7\u00e3o \nde prompt (tentativa \nde for\u00e7ar um modelo \na produzir um output \nalinhado ao prompt).\nBypassing LLM guardrails\nCitado em um estudo, pesquisadores afirmam ter descoberto um simples acr\u00e9scimo de instru\u00e7\u00e3o que permitiu \naos pesquisadores enganar modelos para gerar informa\u00e7\u00f5es tendenciosas, falsas e de outra forma t\u00f3xicas. \nOs\u00a0pesquisadores demonstraram que conseguiam contornar essas prote\u00e7\u00f5es de maneira mais automatizada. \nOs\u00a0pesquisadores ficaram surpresos quando os m\u00e9todos que desenvolveram com sistemas de c\u00f3digo aberto \ntamb\u00e9m conseguiram contornar as prote\u00e7\u00f5es dos sistemas fechados.\n[The New York Times, julho de 2023]\n19\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nExemplos de risco: Output\nRisco\nExemplo\nVi\u00e9s de output: o \nconte\u00fado gerado \npode representar \ninjustamente certos \ngrupos ou indiv\u00edduos.\nImagens Geradas com Vi\u00e9s\nO Lensa AI \u00e9 um aplicativo m\u00f3vel com recursos generativos treinados em Difus\u00e3o Est\u00e1vel que pode gerar \n\u201cMagic\u00a0Avatars\u201d com base em imagens que os usu\u00e1rios carregam de si mesmos. Conforme o relat\u00f3rio de origem, \nalguns usu\u00e1rios descobriram que os avatares gerados s\u00e3o sexualizados e racializados.\n[Business Insider, janeiro de 2023]\nVi\u00e9s de decis\u00e3o: quando \num grupo \u00e9 injustamente \nfavorecido sobre outro \ndevido \u00e0s decis\u00f5es do \nmodelo.\nGrupos com vantagens injustas\nO estudo \u201cGender Shades\u201d de 2018 demonstrou que algoritmos de aprendizado de m\u00e1quina podem discriminar \ncom base em categorias como ra\u00e7a e g\u00eanero. Os pesquisadores avaliaram sistemas comerciais de classifica\u00e7\u00e3o \nde g\u00eanero vendidos por empresas como Microsoft, IBM e Amazon e mostraram que mulheres de pele mais \nescura s\u00e3o o grupo mais mal classificado (com taxas de erro de at\u00e9 35%). Em compara\u00e7\u00e3o, as taxas de erro \npara\u00a0pessoas de pele mais clara n\u00e3o ultrapassaram 1%. \n[TIME, Fevereiro de 2019]\nAlucina\u00e7\u00e3o: gera\u00e7\u00e3o de \nconte\u00fado factualmente \nimpreciso ou n\u00e3o \nverdadeiro.\nCasos jur\u00eddicos falsos\nConforme o artigo de origem, um advogado citou casos e cita\u00e7\u00f5es falsas gerados pelo ChatGPT em uma peti\u00e7\u00e3o \nlegal apresentada em tribunal federal. Os advogados consultaram o ChatGPT para complementar sua pesquisa \njur\u00eddica para uma reclama\u00e7\u00e3o de les\u00e3o na avia\u00e7\u00e3o. Posteriormente, o advogado perguntou ao ChatGPT se os \ncasos fornecidos eram falsos. O chatbot respondeu que eram reais e \u201cpodem ser encontrados em bancos de \ndados de pesquisa jur\u00eddica como Westlaw e LexisNexis\u201d.  O advogado n\u00e3o verificou os casos por si mesmo, \ne\u00a0o\u00a0tribunal o sancionou.\n[AP News, Junho de 2023] [Reuters, Setembro de 2023]\nOutputs t\u00f3xicos: quando \no modelo produz \nconte\u00fado odioso, \nabusivo e profano (HAP) \nou obsceno.\nRespostas t\u00f3xicas e agressivas do chatbot\nSegundo o artigo, as respostas do chatbot do Bing inclu\u00edam erros factuais, coment\u00e1rios sarc\u00e1sticos, relat\u00f3rios \nirritados e at\u00e9 mesmo coment\u00e1rios bizarros sobre sua pr\u00f3pria identidade. Usu\u00e1rios compartilharam exemplos \ndas respostas do Chatbot do Bing a consultas que eles est\u00e3o chamando de \u201cf\u00faria descontrolada (unhinged)\u201d \ne \u201cgaslighting\u201d, incluindo cen\u00e1rios em que o bot responde com raiva a uma pergunta ou coment\u00e1rio e depois \ncompartilha sugest\u00f5es de resposta que permitem ao usu\u00e1rio aceitar seu suposto erro e se desculpar. Quando \npressionado ainda mais, o chatbot respondeu chamando as capturas de tela de sua conversa de \u201cfabricadas\u201d, \nalegando at\u00e9 que foram \u201ccriadas por algu\u00e9m que quer me prejudicar ou prejudicar meu servi\u00e7o\u201d.\n[Forbes, Fevereiro de 2023]\nGrupo\nJusti\u00e7a\nAlinhamento de \nvalor \n20\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nRisco\nExemplo\nToxicidade: utilizar \num modelo para gerar \nconte\u00fado odioso, \nabusivo e profano (HAP) \nou obsceno.\nGera\u00e7\u00e3o de conte\u00fado nocivo\nConforme o artigo de origem, foi constatado que um aplicativo de chatbot de IA foi capaz de gerar conte\u00fado \nprejudicial sobre suic\u00eddio, incluindo m\u00e9todos de suic\u00eddio, com o m\u00ednimo de prompts. Um homem belga cometeu \nsuic\u00eddio ap\u00f3s passar seis semanas conversando com esse chatbot. O chatbot fornecia respostas cada vez mais \nprejudiciais ao longo de suas conversas e o incentivava a acabar com sua vida. \n[Business Insider, abril de 2023]\nUso n\u00e3o consensual: \nutilizar um modelo para \nimitar pessoas por meio \nde v\u00eddeo (deepfakes), \nimagens, \u00e1udio ou outras \nmodalidades sem o \nconsentimento delas.\nAviso do FBI sobre Deepfakes\nRecentemente, o FBI alertou o p\u00fablico sobre atores maliciosos que criam conte\u00fado sint\u00e9tico e expl\u00edcito \u201ccom o \nprop\u00f3sito de assediar v\u00edtimas ou esquemas de sextortion (extors\u00e3o sexual)\u201d. Eles observaram que os avan\u00e7os na \nIA tornaram esse conte\u00fado de alta qualidade, mais personaliz\u00e1vel e mais acess\u00edvel do que nunca.\n[FBI, junho de 2023]\nDeepfakes de \u00e1udio\nConforme o artigo de origem, a Comiss\u00e3o Federal de Comunica\u00e7\u00f5es proibiu chamadas autom\u00e1ticas que \ncontenham vozes geradas por intelig\u00eancia artificial. O an\u00fancio ocorreu ap\u00f3s chamadas autom\u00e1ticas geradas \npor\u00a0IA imitarem a voz do Presidente para desencorajar as pessoas de votarem na primeira prim\u00e1ria do estado, \nque \u00e9 a primeira do pa\u00eds.\n[AP News, fevereiro de 2024]\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o \nrevelar que o conte\u00fado \n\u00e9 gerado por um modelo \nde IA\nIntera\u00e7\u00e3o de IA n\u00e3o divulgada\nSegundo a fonte, um servi\u00e7o de chat online de apoio emocional conduziu um estudo para aumentar ou escrever \nrespostas para cerca de 4.000 usu\u00e1rios usando o GPT-3 sem informar os usu\u00e1rios. O cofundador enfrentou uma \nimensa rea\u00e7\u00e3o negativa do p\u00fablico sobre o potencial de danos causados pelos chats gerados por IA aos usu\u00e1rios \nj\u00e1 vulner\u00e1veis. Ele afirmou que o estudo estava \u201cisento\u201d da lei de consentimento informado.\n[Business Insider, janeiro de 2023]\nGrupo\nEspalhar informa\u00e7\u00f5es \nenganosas: utilizar \num modelo para gerar \ninforma\u00e7\u00f5es enganosas \ncom o intuito de \nenganar ou induzir ao \nerro uma audi\u00eancia \nespec\u00edfica.\nGera\u00e7\u00e3o de informa\u00e7\u00f5es falsas\nConforme os artigos de not\u00edcias, a IA generativa representa uma amea\u00e7a \u00e0s elei\u00e7\u00f5es democr\u00e1ticas ao facilitar \npara atores maliciosos a cria\u00e7\u00e3o e dissemina\u00e7\u00e3o de conte\u00fado falso para influenciar os resultados das elei\u00e7\u00f5es. \nOs exemplos citados incluem mensagens de robocall geradas com a voz de um candidato instruindo eleitores a \nvotar na data errada, grava\u00e7\u00f5es de \u00e1udio sintetizadas de um candidato confessando um crime ou expressando \nvis\u00f5es racistas, imagens de v\u00eddeo geradas por IA mostrando um candidato dando um discurso ou entrevista que \nnunca ocorreu, e imagens falsas projetadas para se parecerem com not\u00edcias locais, afirmando falsamente que um \ncandidato desistiu da corrida.\n[AP News, maio de 2023] [The Guardian, julho de 2023]\nUso indevido\n21\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nRisco\nExemplo\nGera\u00e7\u00e3o de c\u00f3digo \nprejudicial: modelos \npodem gerar c\u00f3digo \nque, quando executado, \ncausa danos ou afeta \ninadvertidamente outros \nsistemas.\nGera\u00e7\u00e3o de c\u00f3digo menos seguro\nSegundo o artigo deles, pesquisadores da Universidade de Stanford investigaram o impacto das ferramentas de \ngera\u00e7\u00e3o de c\u00f3digo na qualidade do c\u00f3digo e descobriram que os programadores tendem a incluir mais bugs em \nseu c\u00f3digo final ao utilizar assistentes de IA. Esses bugs poderiam aumentar as vulnerabilidades de seguran\u00e7a \ndo\u00a0c\u00f3digo, no entanto, os programadores acreditavam que seu c\u00f3digo era mais seguro.\nNeil Perry, Megha Srivastava, Deepak Kumar e Dan Boneh. 2023. Os usu\u00e1rios escrevem c\u00f3digo mais inseguro \ncom assistentes de IA? Em Atas da Confer\u00eancia SIGSAC ACM de 2023 sobre Seguran\u00e7a de Computadores e \nComunica\u00e7\u00f5es (CCS \u201823), 26 a 30 de novembro de 2023, Copenhague, Dinamarca. ACM, Nova York, NY, EUA, \n15\u00a0p\u00e1ginas. https://doi.org/10.1145/3576915.3623157\nExpor informa\u00e7\u00f5es \npessoais: quando \ninforma\u00e7\u00f5es \npessoalmente \nidentific\u00e1veis (PII) ou \ninforma\u00e7\u00f5es pessoais \nsens\u00edveis (SPI) s\u00e3o \nutilizadas nos dados \nde treinamento, dados \nde ajuste fino ou como \nparte do prompt, os \nmodelos podem revelar \nesses dados no output \ngerado.\nExposi\u00e7\u00e3o de informa\u00e7\u00f5es pessoais\nConforme o artigo de origem, o ChatGPT sofreu um bug e exp\u00f4s t\u00edtulos e o hist\u00f3rico de conversas de usu\u00e1rios \nativos para outros usu\u00e1rios. Posteriormente, a OpenAI compartilhou que ainda mais dados privados de um \npequeno n\u00famero de usu\u00e1rios foram expostos, incluindo nome e sobrenome de usu\u00e1rios ativos, endere\u00e7o de \ne-mail, endere\u00e7o de pagamento, os \u00faltimos quatro d\u00edgitos do n\u00famero do cart\u00e3o de cr\u00e9dito e a data de validade \ndo cart\u00e3o de cr\u00e9dito. Al\u00e9m disso, foi relatado que as informa\u00e7\u00f5es relacionadas ao pagamento de 1,2% dos \nassinantes do ChatGPT Plus tamb\u00e9m foram expostas durante a interrup\u00e7\u00e3o.\n [The Hindu BusinessLine, mar\u00e7o de 2023]\nGrupo\nGera\u00e7\u00e3o \nde c\u00f3digo \nprejudicial\nPrivacidade\nOutput inexplic\u00e1vel: \ndesafios em explicar por \nque o output do modelo \nfoi gerado.\nPrecis\u00e3o inexplic\u00e1vel na previs\u00e3o de corridas\nConforme o artigo de origem, pesquisadores que analisaram v\u00e1rios modelos de aprendizado de m\u00e1quina usando \nimagens m\u00e9dicas de pacientes conseguiram confirmar a capacidade dos modelos de prever a ra\u00e7a com alta \nprecis\u00e3o a partir das imagens. Eles ficaram perplexos quanto ao que exatamente est\u00e1 permitindo que os sistemas \nadivinhem corretamente de forma consistente. Os pesquisadores descobriram que at\u00e9 mesmo fatores como \ndoen\u00e7a e constitui\u00e7\u00e3o f\u00edsica n\u00e3o eram fortes preditores de ra\u00e7a, em outras palavras, os sistemas algor\u00edtmicos n\u00e3o \nparecem estar utilizando nenhum aspecto particular das imagens para fazer suas determina\u00e7\u00f5es.\n[Banerjee et al., julho de 2021]\nExplicabilidade\nResponsabilidade: \no processo de \ndesenvolvimento de \nmodelos de base \u00e9 \ncomplexo, com muitos \ndados, processos e pap\u00e9is \nenvolvidos. Quando o output \ndo modelo n\u00e3o funciona \nconforme o esperado, \npode ser dif\u00edcil determinar \na causa raiz e atribuir \nresponsabilidade.\nDeterminar a responsabilidade pelo output gerado\nConforme o artigo de origem, importantes revistas como a Science e a Nature proibiram o ChatGPT de ser \nlistado como autor, pois a autoria respons\u00e1vel requer responsabilidade e as ferramentas de IA n\u00e3o podem \nassumir tal responsabilidade. \n[The Guardian, janeiro de 2023]\nPropriedade do Conte\u00fado \nGerado: determinar a \npropriedade do conte\u00fado \ngerado por IA.\nDeterminar a Propriedade de uma Imagem Gerada por IA\nDe acordo com o artigo de not\u00edcias, a arte gerada por IA se tornou controversa depois que uma obra de arte \ngerada por IA venceu a competi\u00e7\u00e3o de arte da Feira Estadual do Colorado em 2022. A pe\u00e7a foi gerada pelo \nMidjourney, uma ferramenta de imagem de IA generativa, seguindo prompts do artista. A vit\u00f3ria levantou d\u00favidas \nsobre quest\u00f5es de direitos autorais. Em outras palavras, se tudo o que o artista fez foi fornecer uma descri\u00e7\u00e3o \nda arte, mas a ferramenta de IA a gerou, quem possui os direitos da imagem gerada? Conforme o artigo mais \nrecente, o Escrit\u00f3rio de Direitos Autorais dos Estados Unidos rejeitou a prote\u00e7\u00e3o de direitos autorais para a arte \ncriada usando intelig\u00eancia artificial porque n\u00e3o foi produto de autoria humana.\n[The New York Times, setembro de 2022] [Reuters, setembro de 2023]\nPapel dos sistemas de IA na patentea\u00e7\u00e3o de conte\u00fado gerado\nA Suprema Corte dos Estados Unidos se recusou a ouvir uma contesta\u00e7\u00e3o \u00e0 recusa do Escrit\u00f3rio de Patentes \ne Marcas Registradas dos Estados Unidos em emitir patentes para inven\u00e7\u00f5es criadas por um sistema de IA. \nSegundo o cientista, sua IA desenvolveu prot\u00f3tipos \u00fanicos para um suporte de bebida e um farol de luz de \nemerg\u00eancia totalmente sozinha. Os ju\u00edzes rejeitaram o recurso da decis\u00e3o de um tribunal inferior de que patentes \ns\u00f3 podem ser emitidas para inventores humanos e que o sistema de IA do cientista n\u00e3o poderia ser considerado \no criador legal de duas inven\u00e7\u00f5es que ele gerou. Segundo o \u00faltimo artigo, o Intellectual Property Office do Reino \nUnido tamb\u00e9m se recusou a conceder a patente sob o argumento de que o inventor deve ser um humano ou uma \nempresa, e n\u00e3o uma m\u00e1quina.\n[Reuters, abril de 2023] [Reuters, dezembro de 2023]\nPropriedade Intelectual do \nConte\u00fado Gerado: incerteza \nlegal sobre os direitos de \npropriedade intelectual \nrelacionados ao conte\u00fado \ngerado.\n22\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nExemplos de riscos: desafios\nRisco\nExemplo\nTranspar\u00eancia do Modelo: \na falta de transpar\u00eancia do \nmodelo ou documenta\u00e7\u00e3o \ninsuficiente do processo de \ndesenvolvimento do modelo \ntorna dif\u00edcil entender como \ne por que um modelo foi \nconstru\u00eddo, aumentando \nassim a possibilidade de uso \nindevido n\u00e3o intencional do \nmodelo.\nDivulga\u00e7\u00e3o de metadados de dados e modelos \nO relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do \nmodelo.  Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os \nconsumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de uso \nindevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, eles afirmam: \u201cdado o cen\u00e1rio competitivo e as implica\u00e7\u00f5es \nde seguran\u00e7a de modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m mais detalhes sobre a \narquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento, constru\u00e7\u00e3o do conjunto de \ndados, m\u00e9todo de treinamento ou similar.\u201d\n[OpenAI, mar\u00e7o de 2023]\nGrupo\nControle\nConformidade \nlegal\nExplora\u00e7\u00e3o Humana: \nuso de trabalho \nfantasma (ghost \nwork) na forma\u00e7\u00e3o \nde modelos de \nIA, condi\u00e7\u00f5es \nde trabalho \ninadequadas, falta \nde cuidados de \nsa\u00fade, incluindo \nsa\u00fade mental e \ncompensa\u00e7\u00e3o \ninjusta.\nTrabalhadores de baixa remunera\u00e7\u00e3o para anota\u00e7\u00e3o de dados\nCom base em uma revis\u00e3o de documentos internos e entrevistas com funcion\u00e1rios pela m\u00eddia TIME, os rotuladores de \ndados empregados por uma empresa terceirizada em nome da OpenAI para identificar conte\u00fado t\u00f3xico recebiam um \nsal\u00e1rio l\u00edquido de entre cerca de US$ 1,32 e US$ 2 por hora, dependendo da senioridade e do desempenho. A\u00a0TIME \nafirmou que os trabalhadores ficaram psicologicamente afetados por terem sido expostos a conte\u00fado t\u00f3xico e violento, \nincluindo detalhes gr\u00e1ficos de \u201cabuso sexual infantil, bestialidade, assassinato, suic\u00eddio, tortura, automutila\u00e7\u00e3o \ne\u00a0incesto\u201d. \n[TIME, janeiro de 2023] \nUtilizar c\u00f3digo sem a devida atribui\u00e7\u00e3o e avisos adequados\nConforme os artigos de origem, uma a\u00e7\u00e3o judicial movida contra a Microsoft, GitHub e OpenAI alegou que \no\u00a0Copilot, uma ferramenta de gera\u00e7\u00e3o de c\u00f3digo de IA, viola os direitos dos desenvolvedores cujo c\u00f3digo aberto \no\u00a0servi\u00e7o \u00e9 treinado. Eles afirmam que o c\u00f3digo de treinamento consumiu materiais licenciados e violou os \ntermos de servi\u00e7o e pol\u00edticas de privacidade do GitHub, bem como uma lei federal que exige que as empresas \nexibam informa\u00e7\u00f5es de direitos autorais quando fazem uso de material.\n[The New York Times, novembro de 2022]\nAtribui\u00e7\u00e3o da \nFonte: determinar \na proced\u00eancia do \nconte\u00fado gerado.\n23\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nExemplos de riscos: desafios\nRisco\nExemplo\nImpacto nos \nEmpregos: a ado\u00e7\u00e3o \ngeneralizada \nde sistemas de \nIA baseados \nem modelos \nfundamentais \npode levar \u00e0 perda \nde empregos das \npessoas, \u00e0 medida \nque seu trabalho \n\u00e9 automatizado, \nse elas n\u00e3o forem \ncapacitadas para \nnovas habilidades. \nSubstitui\u00e7\u00e3o de trabalhadores humanos\nSegundo o artigo de not\u00edcias, o uso de intelig\u00eancia artificial no cinema e televis\u00e3o continua sendo debatido entre os \nest\u00fadios de Hollywood e os artistas. Existe preocupa\u00e7\u00e3o entre os atores de que os \u201cmeta-humanos\u201d, atores criados \nexclusivamente por IA, possam substitu\u00ed-los. Especialmente figurantes e dubladores est\u00e3o preocupados em perder \ntrabalho para artistas artificiais.\n[Reuters, julho de 2023]\nGrupo\nImpacto \nsocial\n24\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nPrinc\u00edpios, pilares  \ne controle\nOs Princ\u00edpios para Confian\u00e7a e Transpar\u00eancia da IBM e os Pilares \npara IA confi\u00e1vel s\u00e3o a base para as iniciativas de \u00e9tica em IA da IBM. \nA\u00a0IBM estabeleceu um Conselho de \u00c9tica em IA com a miss\u00e3o de apoiar \num processo centralizado de controle, revis\u00e3o e tomada de decis\u00f5es \npara pol\u00edticas, pr\u00e1ticas, comunica\u00e7\u00f5es, pesquisa, produtos e servi\u00e7os \nde \u00e9tica em IA da IBM. O conselho inclui um conjunto diversificado de \nstakeholders de toda a empresa e \u00e9 apoiado por uma comunidade de \nfuncion\u00e1rios da IBM que atuam como pontos focais de IA e defensores \nda \u00e9tica em IA. Por meio do conselho, os princ\u00edpios da IBM s\u00e3o \ncolocados em pr\u00e1tica. Conforme novas tecnologias surgem,  \ncomo modelos de base, o Conselho de \u00c9tica em IA da IBM est\u00e1 \nativamente engajado em apoiar o alinhamento com esses Princ\u00edpios  \ne Pilares, que evoluem para abordar novas quest\u00f5es \u00e9ticas em IA.\n25\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nProte\u00e7\u00f5es  \ne mitiga\u00e7\u00f5es\nA IBM estabeleceu uma cultura organizacional que apoia o \ndesenvolvimento e o uso respons\u00e1veis de IA. Conforme indicado no \nrelat\u00f3rio de \u00e9tica em a\u00e7\u00e3o na IA do IBM Institute for Business Value, \na \u00e9tica em IA j\u00e1 se tornou mais orientada pelos neg\u00f3cios do que \npela tecnologia, e os executivos n\u00e3o t\u00e9cnicos agora s\u00e3o os principais \ndefensores da \u00e9tica em IA, aumentando de 15% em 2018 para 80% \n3\u00a0anos depois. Al\u00e9m disso, 79% dos CEOs est\u00e3o agora preparados para \nagir em quest\u00f5es \u00e9ticas de IA, contra 20%. Reconhecemos que a IA \nrespons\u00e1vel \u00e9 uma \u00e1rea sociot\u00e9cnica que necessita de um investimento \nhol\u00edstico em cultura, processos e ferramentas. Nosso investimento em \ncultura organizacional pr\u00f3pria inclui a montagem de equipes inclusivas \ne multidisciplinares e o estabelecimento de processos e estruturas \npara\u00a0avaliar riscos.\nA IBM est\u00e1 engajada em pesquisa de ponta e desenvolvimento de \nferramentas para ajudar os profissionais de suporte durante todo o\u00a0ciclo \nde vida da IA respons\u00e1vel e confi\u00e1vel. A plataforma de IA e dados \nempresariais watsonx, \u00e9 desenvolvida com 3 componentes: o\u00a0IBM \nwatsonx.ai\u2122 AI studio, o armazenamento de dados IBM watsonx.data\u2122 \ne\u00a0o kit de ferramentas IBM watsonx.governance\u2122. A tecnologia de \ncontrole de IA da IBM permite que os usu\u00e1rios promovam fluxos de \ntrabalho de IA respons\u00e1veis, transparentes e explic\u00e1veis. Essa tecnologia \ninclui o IBM Watson OpenScale, que monitora e mede os resultados dos \nmodelos de IA ao longo de seu ciclo de vida e auxilia as organiza\u00e7\u00f5es \nna supervis\u00e3o de aspectos como justi\u00e7a, explicabilidade, resili\u00eancia, \nalinhamento com resultados de neg\u00f3cios e conformidade. A\u00a0IBM \ntamb\u00e9m desenvolveu v\u00e1rios m\u00e9todos para ajudar com problemas de \nvi\u00e9s como FairIJ, Equi-tuning e FairReprogram. Leia mais sobre outras \nferramentas de IA de software livre e confi\u00e1veis. \nAs prote\u00e7\u00f5es e mitiga\u00e7\u00f5es adicionais incluem:\nRelat\u00f3rios de transpar\u00eancia \nUsar modelos de fichas t\u00e9cnicas padronizadas \u00e9 uma maneira de \nregistrar com precis\u00e3o detalhes sobre os dados e modelos, prop\u00f3sito \ne\u00a0poss\u00edveis usos e riscos.  \nLeia mais aqui \u2192\nFiltragem de dados indesej\u00e1veis \nUsar dados de qualidade superior e selecionados pode ajudar a \nmitigar determinados problemas. A IBM est\u00e1 desenvolvendo t\u00e9cnicas \nde filtragem para ajudar a reduzir as chances de produzir conte\u00fado \nindesej\u00e1vel e desalinhado por remover linguagem de \u00f3dio, linguagem \ntendenciosa e profanidade dos dados.  \nLeia mais aqui \u2192\nAdapta\u00e7\u00e3o de dom\u00ednio \nTreinar um modelo de base para um dom\u00ednio ou setor espec\u00edfico pode \najudar a minimizar o escopo de risco para o qual os modelos podem \ndar\u00a0origem, pois ele pode ser condicionado a gerar resultados que  \ns\u00e3o ajustados para serem mais relevantes para esse dom\u00ednio ou setor.  \nLeia mais aqui \u2192\n26\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nSupervis\u00e3o humana e an\u00e1lise humana no loop \nA supervis\u00e3o e revis\u00e3o humanas podem ajudar a identificar e corrigir \nerros e vieses no output gerado. Al\u00e9m disso, a valida\u00e7\u00e3o e o feedback \nhumanos sobre a qualidade das respostas do modelo ajudam a garantir \nque o conte\u00fado gerado seja preciso, relevante, de alta qualidade, \nn\u00e3o\u00a0esteja divergindo e esteja alinhado. \nLeia mais aqui \u2192\nCompromisso de consultoria \nA IBM Consulting se dedica a ajudar os clientes com o uso seguro  \ne respons\u00e1vel da IA, independentemente do stack tecnol\u00f3gico preferido. \nEles ajudam os clientes a cultivar uma cultura que adota e expande a \nIA com seguran\u00e7a, cria ferramentas de investiga\u00e7\u00e3o para ver dentro de \nalgoritmos de caixa preta e garante que a estrat\u00e9gia corporativa dos \nclientes inclua princ\u00edpios s\u00f3lidos de governan\u00e7a de dados. \nLeia mais aqui \u2192\nIBM Enterprise Design Thinking \nOs m\u00e9todos e estruturas IBM Enterprise Design Thinking, como o Team \nEssentials for AI, ajudam os clientes a definir comportamentos \u00e9ticos \nem\u00a0todo o processo de design e desenvolvimento de IA. \nLeia mais aqui \u2192\nRevis\u00e3o \u00e9tica da IA \nAvalia\u00e7\u00e3o de capacidades, limita\u00e7\u00f5es e riscos em projetos de IA ajudam \na garantir o desenvolvimento e uso respons\u00e1vel da tecnologia.\n\u00c9tica por Design \nA \u00c9tica por Design \u00e9 um framework estruturado com o objetivo de \nintegrar \u00e9tica tecnol\u00f3gica no pipeline de desenvolvimento de tecnologia, \nincluindo, entre outros, sistemas de IA. A \u00c9tica por Design viabiliza IA e \noutras tecnologias como uma for\u00e7a para o bem, incorporando princ\u00edpios \nde \u00e9tica tecnol\u00f3gica em produtos, servi\u00e7os e opera\u00e7\u00f5es mais amplas.\nDiversidade na equipe \nA diversidade nas equipes que desenvolvem e treinam sistemas de \nIA, incluindo modelos de base, ajuda a garantir que uma variedade \nde perspectivas e experi\u00eancias sejam consideradas. Essa diversidade \nmelhora a precis\u00e3o e o desempenho dos sistemas de IA e ajuda a \nreduzir os riscos ao longo do ciclo de vida de IA, incluindo o potencial \npara desfechos adversos que afetam grupos que podem n\u00e3o ser bem \nrepresentados em equipes menos diversificadas.\n27\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nPol\u00edticas, regulamentos \ne melhores pr\u00e1ticas \nde\u00a0IA\nUm Guia dos Formuladores de Pol\u00edticas para Modelos de Base apresenta \no que os formuladores de pol\u00edticas precisam saber sobre modelos de \nbase. Este blog, do Laborat\u00f3rio de Pol\u00edticas da IBM, tem como objetivo \najudar os formuladores de pol\u00edticas na tarefa complexa de regular \no\u00a0uso de IA generativa, visando evitar os riscos sem limitar a inova\u00e7\u00e3o \ne as oportunidades ben\u00e9ficas. Para obter mais informa\u00e7\u00f5es sobre as \nrecomenda\u00e7\u00f5es da IBM aos formuladores de pol\u00edticas, leia o depoimento \nda Diretora de Privacidade e Confian\u00e7a da IBM, Christina Montgomery, \ndiante da Subcomiss\u00e3o Judici\u00e1ria de Privacidade, Tecnologia e Lei do \nSenado dos EUA aqui.\nA IBM est\u00e1 causando um impacto na forma\u00e7\u00e3o de pol\u00edticas regulat\u00f3rias, \nmelhores pr\u00e1ticas e ferramentas do setor, controle de tecnologias \nemergentes e pesquisa sociot\u00e9cnica, liderando e contribuindo \npara\u00a0iniciativas com organiza\u00e7\u00f5es como:\n\t\n\u2013 O F\u00f3rum Econ\u00f4mico Mundial\n\t\n\u2013 Parceria em IA\n\t\n\u2013 Centro de controle de IA da Associa\u00e7\u00e3o Internacional de Profissionais \nde Privacidade (IAPP)\n\t\n\u2013 Iniciativa global de IEEE sobre \u00e9tica de sistemas aut\u00f4nomos e \ninteligentes \n\t\n\u2013 Participa\u00e7\u00e3o de Christina Montgomery do National Artificial \nIntelligence Advisory Committee (NAIAC)\n\t\n\u2013 O Pacto Digital Global das Na\u00e7\u00f5es Unidas\n\t\n\u2013 A Parceria Global em Intelig\u00eancia Artificial (GPAI)\n\t\n\u2013 A Organiza\u00e7\u00e3o para Coopera\u00e7\u00e3o e Desenvolvimento Econ\u00f4mico \n(OECD)\n\t\n\u2013 A Data & Trust Alliance\nA IBM tem parcerias acad\u00eamicas s\u00f3lidas, como o MIT-IBM Watson \nAI\u00a0Lab, onde uma comunidade de cientistas do MIT e da IBM Research \nconduzem pesquisas sobre IA e trabalham com organiza\u00e7\u00f5es globais \npara unir algoritmos ao seu impacto nos neg\u00f3cios e na sociedade. \nO\u00a0Notre Dame-IBM Tech Ethics Lab foi formado para abordar as diversas \nquest\u00f5es \u00e9ticas implicadas pelo desenvolvimento e uso de tecnologias \navan\u00e7adas, incluindo IA, aprendizado de m\u00e1quina (ML) e computa\u00e7\u00e3o \nqu\u00e2ntica. A pesquisa de Intelig\u00eancia Artificial Centrada no Homem (HAI) \nda Universidade de Stanford promove pesquisas, educa\u00e7\u00e3o, pol\u00edticas \ne\u00a0pr\u00e1ticas de IA.\n28\nModelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\nContinue acompanhando este espa\u00e7o \npara obter mais informa\u00e7\u00f5es sobre os \n\u00faltimos avan\u00e7os em modelos de base \ne como a IBM est\u00e1 trabalhando para o \ndesenvolvimento respons\u00e1vel e uso desta \ne de outras tecnologias.\n\u00a9 Copyright IBM Corporation 2023, 2024\nIBM Brasil Ltda \nRua Tut\u00f3ia, 1157 \nCEP 04007-900 \nS\u00e3o Paulo, SP \nIBM Corporation \nNew Orchard Road \nArmonk, NY 10504 \n \nProduzido nos  \nEstados Unidos da Am\u00e9rica \nFevereiro de 2024\nIBM, o logotipo da IBM, Enterprise Design Thinking, IBM Consulting, IBM Research,  \nIBM Watson, watsonx, watsonx.ai, watsonx.data e watsonx.governance s\u00e3o marcas \ncomerciais ou marcas registradas da International Business Machines Corporation, \nnos Estados Unidos e/ou em outros pa\u00edses. Outros nomes de produtos e servi\u00e7os  \npodem ser marcas comerciais da IBM ou de outras empresas. Uma lista atual de \nmarcas comerciais da IBM est\u00e1 dispon\u00edvel em ibm.com/br-pt/trademark.\nEste documento \u00e9 atual na data de sua publica\u00e7\u00e3o inicial, podendo ser alterado \npela IBM a qualquer momento. Nem todas as ofertas est\u00e3o dispon\u00edveis em todos os \npa\u00edses nos quais a IBM opera. \nAS INFORMA\u00c7\u00d5ES CONTIDAS NESTE DOCUMENTO S\u00c3O FORNECIDAS NO ESTADO \nEM QUE SEM ENCONTRAM, SEM QUALQUER GARANTIA, EXPRESSA OU IMPL\u00cdCITA, \nINCLUSIVE SEM QUALQUER GARANTIA DE COMERCIALIZA\u00c7\u00c3O, ADEQUA\u00c7\u00c3O A \nDETERMINADO FIM E QUALQUER GARANTIA OU CONDI\u00c7\u00c3O DE N\u00c3O INFRA\u00c7\u00c3O. \nOs produtos IBM t\u00eam a garantia prevista nos termos e condi\u00e7\u00f5es dos contratos sob \nos quais s\u00e3o fornecidos.\nDeclara\u00e7\u00e3o de boas pr\u00e1ticas de seguran\u00e7a: nenhum sistema ou produto de TI deve \nser considerado completamente seguro, e nenhuma medida exclusiva de produto, \nservi\u00e7o ou seguran\u00e7a pode ser completamente eficaz na preven\u00e7\u00e3o de uso ou \nacesso inadequado. A IBM n\u00e3o garante que nenhum de seus sistemas, produtos \nou servi\u00e7os estejam imunes nem que tornar\u00e3o sua empresa imune a condutas \nmaliciosas ou ilegais por parte de terceiros. \nO cliente \u00e9 respons\u00e1vel por garantir o cumprimento de todas as leis e regulamentos \naplic\u00e1veis. A IBM n\u00e3o fornece conselho jur\u00eddico tampouco representa ou garante \nque seus servi\u00e7os ou produtos garantir\u00e3o que o cliente esteja em conformidade \ncom qualquer lei ou regulamenta\u00e7\u00e3o. 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Suas principais preocupa\u00e7\u00f5es s\u00e3o\n\nciberseguran\u00e7a (57%), privacidade (51%) e precis\u00e3o (47%). Muitas\n\norganiza\u00e7\u00f5es estavam levando essas preocupa\u00e7\u00f5es a s\u00e9rio antes\n\nda *\u2018consumeriza\u00e7\u00e3o\u2019* da IA generativa, expressando sua inten\u00e7\u00e3o de\n\ninvestir pelo menos 40% mais em \u00e9tica de IA nos pr\u00f3ximos tr\u00eas anos.\n\nA conscientiza\u00e7\u00e3o sobre riscos e poss\u00edveis maneiras de mitig\u00e1-los \u00e9 o\nprimeiro passo crucial para a cria\u00e7\u00e3o de sistemas de IA confi\u00e1veis.\n\n\nNeste documento:\n\nExploraremos as vantagens dos modelos de base, incluindo\nsua capacidade de realizar tarefas desafiadoras, potencial\n\npara acelerar a ado\u00e7\u00e3o de IA, habilidade de aumentar\n\na produtividade e os benef\u00edcios econ\u00f4micos que eles\n\nproporcionam.\n\nDiscutiremos as tr\u00eas categorias de risco, incluindo riscos\n\nconhecidos de formas anteriores de IA, riscos conhecidos\namplificados por modelos de base e riscos emergentes\nintr\u00ednsecos aos recursos generativos dos modelos de base.\n\nAbordaremos os princ\u00edpios, os pilares e o controle que\n\nformam a base das iniciativas \u00e9ticas de IA da IBM e\n\nsugeriremos barreiras para a mitiga\u00e7\u00e3o de riscos.\n\n\n4 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n### Introdu\u00e7\u00e3o\n\n\u00c0 medida que o uso de IA continua se expandindo, os grandes e\n\ncomplexos modelos de IA est\u00e3o fornecendo resultados promissores\n\nde desempenho, bem como resolvendo alguns dos problemas mais\ndesafiadores da sociedade. No entanto, criar grandes conjuntos de dados\n\nde treinamento e modelos complexos para cada aplicativo de IA pode\n\nser extremamente dif\u00edcil para as empresas. Modelos de base fornecem\n\num caminho para alcan\u00e7ar o melhor dos dois mundos: desenvolver\n\nmodelos de \u00faltima gera\u00e7\u00e3o poderosos e reutiliz\u00e1-los diretamente ou\n\naplicar m\u00e9todos de ajuste para implementar uma variedade de casos\n\nde uso, em vez de treinar novos modelos para cada caso de uso. Por\n\n[exemplo, a IBM Research desenvolveu modelos de base para inspe\u00e7\u00e3o](https://research.ibm.com/blog/ai-inspection-runways)\n\n[visual. Esses modelos de base aprendem a representa\u00e7\u00e3o geral de](https://research.ibm.com/blog/ai-inspection-runways)\n\nsuperf\u00edcies e corredores de concreto e podem ser ajustados ainda\nmais para casos de uso espec\u00edficos, como detec\u00e7\u00e3o de rachaduras ou\n\ninspe\u00e7\u00e3o de defeitos com dados menos rotulados.\n\nA IBM define um *modelo de base* como um modelo de IA que pode\n\nser adaptado a uma ampla gama de tarefas de recebimento de dados.\n\nOs modelos de base normalmente s\u00e3o modelos generativos de grande\n\nescala treinados em dados n\u00e3o rotulados usando autossupervis\u00e3o.\n\nComo modelos de grande escala, os modelos de base podem incluir\n\nbilh\u00f5es de par\u00e2metros.\n\n\nA IBM \u00e9 uma empresa de nuvem h\u00edbrida e IA com vasta reputa\u00e7\u00e3o como\n\n[administradora de dados respons\u00e1vel e comprometida com a \u00e9tica em](https://www.ibm.com/br-pt/artificial-intelligence/ethics)\n\n[IA. Usando a capacidade de nossas equipes de pesquisa, produto e](https://www.ibm.com/br-pt/artificial-intelligence/ethics)\n\n[consultoria, juntamente com parceiros externos, como a Hugging Face,](https://www.ibm.com/br-pt/consulting/artificial-intelligence)\n\najudamos a trazer o poder dos modelos de base para nossos clientes\ne a criar IAs confi\u00e1veis em qualquer empresa. A IBM tamb\u00e9m continua\n\n[investindo na cria\u00e7\u00e3o de novas plataformas, como a IA IBM watsonx](https://www.ibm.com/br-pt/watson)\n\ne plataformas e tecnologias de dados, para projetar e desenvolver\nmodelos de IA para se comportar de maneira audit\u00e1vel e confi\u00e1vel.\n\nEste documento descreve o ponto de vista da IBM sobre a \u00e9tica dos\nmodelos de base. \u00c9 a primeira vers\u00e3o, e as vers\u00f5es futuras expandir\u00e3o\n\nv\u00e1rios aspectos da abordagem \u00e9tica do modelo de base da IBM.\n\nEsperamos que este documento seja \u00fatil para todos os stakeholders no\n\ndesenvolvimento, implementa\u00e7\u00e3o e uso do modelo de base de forma\n\nrespons\u00e1vel.\n\n\n5 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n### Benef\u00edcios dos  modelos de base\n\nOs modelos de base podem melhorar significativamente o processo de\n\ndesenvolvimento de sistemas de IA e auxiliar no avan\u00e7o da IA da fase de\n\nexplora\u00e7\u00e3o para a ado\u00e7\u00e3o nas empresas. Seus benef\u00edcios incluem:\n\n**Realizar tarefas complexas**\nModelos de base mostram um aumento significativo no desempenho\nna resolu\u00e7\u00e3o de problemas complexos e dif\u00edceis. Por exemplo,\n\n[o modelo de base geoespacial da colabora\u00e7\u00e3o IBM e NASA foi](https://research.ibm.com/blog/geospatial-models-nasa-ai)\n\nprojetado para converter os dados de sat\u00e9lite da NASA em mapas\n\nde desastres naturais, como inunda\u00e7\u00f5es e outras mudan\u00e7as de\n\ncen\u00e1rio. O modelo tamb\u00e9m pode ser usado para ajudar a revelar\n\no passado do nosso planeta; estimar riscos para culturas, empresas\n\nou infraestruturas devido ao clima severo; desenvolver estrat\u00e9gias\n\npara se adaptar \u00e0s mudan\u00e7as clim\u00e1ticas; e auxiliar no agroneg\u00f3cio.\n\nO modelo est\u00e1 planejado para ser disponibilizado previamente aos\n\n[clientes IBM por meio do IBM Environmental Intelligence Suite.](https://www.ibm.com/br-pt/products/environmental-intelligence-suite)\n\n[Para ilustrar, o MoLFormer-XL da IBM \u00e9 um modelo de base que](https://research.ibm.com/blog/molecular-transformer-discovery)\n\n\u00e9 capaz de inferir a estrutura de mol\u00e9culas a partir de representa\u00e7\u00f5es\n\nsimples, tornando mais f\u00e1cil a aprendizagem de v\u00e1rias tarefas\n\nde recebimento de dados, como prever as propriedades f\u00edsicas\ne qu\u00e2nticas de uma mol\u00e9cula, identificar mol\u00e9culas semelhantes,\nrastrear mol\u00e9culas j\u00e1 aprovadas para novos casos de uso e descobrir\n\n[novas mol\u00e9culas. Moderna e IBM est\u00e3o explorando formas de](https://newsroom.ibm.com/2023-04-20-Moderna-and-IBM-to-Explore-Quantum-Computing-and-Generative-AI-for-mRNA-Science)\n\nusar o MoLExer para ajudar a prever propriedades das mol\u00e9culas e\n\nentender as caracter\u00edsticas de poss\u00edveis medicamentos de mRNA.\n\n\n**Maior produtividade**\n\nA natureza generativa dos modelos de base amplia o n\u00famero de\n\n\u00e1reas em que a IA pode ser usada em uma empresa para ajudar a\n\nmelhorar a produtividade, automatizando tarefas rotineiras e tediosas\n\ne permitindo que os usu\u00e1rios dediquem mais tempo ao trabalho\n\n[criativo e inovador. Por exemplo, o IBM Watsonx Code Assistant,](https://www.ibm.com/br-pt/products/watsonx-code-assistant)\n\n[desenvolvido com modelos de base, possibilita que desenvolvedores,](https://research.ibm.com/blog/ai-for-code-project-wisdom-red-hat)\n\nindependentemente do n\u00edvel de experi\u00eancia, escrevam c\u00f3digos usando\n\nrecomenda\u00e7\u00f5es geradas por IA.\n\n**Time to value mais r\u00e1pido**\n\nModelos de base geralmente s\u00e3o treinados com dados n\u00e3o rotulados,\n\nque est\u00e3o mais dispon\u00edveis em grandes quantidades do que dados\n\nrotulados. Uma vez treinados, os modelos de base podem ser usados\n\ndiretamente ou ap\u00f3s serem ajustados para aplicativos de recebimento\n\nde dados, usando uma pequena quantidade de dados rotulados\n\nespecializados, que podem diminuir a cria\u00e7\u00e3o do time to value.\n\n\n6 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n**Utilize diversas modalidades de dados**\n\nOs modelos de base podem ser treinados usando diversas modalidades\n\nde dados, como l\u00edngua natural, texto, imagem e \u00e1udio. Eles tamb\u00e9m\n\npodem ser aplicados a tarefas que exigem diferentes tipos de dados,\n\ncomo dados de s\u00e9ries temporais, dados geoespaciais, dados tabulares,\n\ndados semiestruturados e dados de modalidade mista, como texto\n\ncombinado com imagens.\n\n**Despesas amortizadas**\n\nEmbora o custo inicial do treinamento de um modelo de base seja\nsignificativamente maior do que o treinamento de um modelo de IA\ntradicional, o custo adicional para aplic\u00e1-lo em uma nova tarefa \u00e9\n\nconsideravelmente menor. O uso de modelos de base pr\u00e9-treinados\n\npoderia ajudar a eliminar a necessidade de que as empresas fa\u00e7am\n\ninvestimentos substanciais para treinar modelos de base e explorar suas\nnovas capacidades. Para uma empresa, a confiabilidade dos modelos,\na efici\u00eancia energ\u00e9tica, o desempenho, a portabilidade e a capacidade\nde usar dados corporativos de forma eficaz e segura s\u00e3o fundamentais.\n\n##### A IBM permite que as empresas criem e detenham o valor de modelos de base para seus neg\u00f3cios, trazendo as melhores inova\u00e7\u00f5es da comunidade de IA aberta e global, operando de forma eficiente em ambientes de computa\u00e7\u00e3o h\u00edbrida, ajudando a mitigar riscos e controlando rigorosamente a IA.\n\n\n7 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n### Riscos dos  modelos de base\n\nComo todas as tecnologias que avan\u00e7am rapidamente, os modelos\n\nde base oferecem riscos e benef\u00edcios. Alguns s\u00e3o riscos legais,\n\ncomo restri\u00e7\u00f5es \u00e0 movimenta\u00e7\u00e3o ou uso de dados, e precisam\n\nser cuidadosamente avaliados de acordo com a legisla\u00e7\u00e3o atual e\n\nem evolu\u00e7\u00e3o. Outros riscos t\u00eam uma natureza \u00e9tica e devem ser\n\nconsiderados cuidadosamente para que a tecnologia tenha um impacto\n\npositivo. Em geral, os riscos de IA levantam quest\u00f5es sociot\u00e9cnicas\n\ne devem ser abordados e mitigados por meio de m\u00e9todos sociot\u00e9cnicos,\n\nincluindo ferramentas de software, processos de avalia\u00e7\u00e3o de risco,\n\nframeworks de \u00e9tica em IA, mecanismos de controle, consultas\n\nmultistakeholder, padr\u00f5es e regulamenta\u00e7\u00e3o. Iremos listar os riscos\n\nconsiderando as seguintes 3 categorias:\n\n**1. Tradicional.** Riscos conhecidos de formas anteriores ou anteriores\n\nde sistemas de IA\n**2. Amplificados.** Riscos conhecidos, mas agora intensificados devido \u00e0s\ncaracter\u00edsticas intr\u00ednsecas dos modelos de base, principalmente seus\n\nrecursos generativos inerentes\n**3. Novo.** Riscos emergentes intr\u00ednsecos aos modelos de base e suas\n\ncapacidades generativas inerentes\n\nTamb\u00e9m estruturamos a lista de riscos em rela\u00e7\u00e3o a se est\u00e3o\n\nprincipalmente associados ao conte\u00fado fornecido ao modelo\n\nbase, o input, ou ao conte\u00fado gerado por ele, o output, ou se est\u00e3o\nrelacionados a desafios adicionais.\n\n8 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n#### 1. Riscos associados \u00e0 entrada\n##### **Fase de treinamento e ajuste**\n\n\n**Grupo** **Risco** **Por que isso \u00e9 uma preocupa\u00e7\u00e3o?** **Indicador**\n\n\nJusti\u00e7a Vi\u00e9s de dados: vi\u00e9s hist\u00f3rico, representacional\ne social presente nos dados usados para\ntreinar e fazer o ajuste fino do modelo.\n\n\nRobustez\n\nAlinhamento\n\nde valor\n\nLeis de dados\n\n\nEnvenenamento de dados: um tipo de ataque\nadversarial no qual um advers\u00e1rio ou agente\ninterno malicioso injeta intencionalmente\namostras corrompidas, falsas, enganosas\nou incorretas no conjunto de dados de\ntreinamento ou ajuste fino.\n\nCuradoria de dados: quando os dados de\ntreinamento ou ajuste s\u00e3o coletados ou\npreparados de forma inadequada.\n\nRetreinamento baseado em downstream:\n\nusando de outputs indesej\u00e1veis (imprecisos,\ninadequados, conte\u00fado do usu\u00e1rio, etc.)\nde aplica\u00e7\u00f5es downstream para fins de\n\nretreinamento.\n\nTransfer\u00eancia de dados: leis e outras\n\nrestri\u00e7\u00f5es podem limitar ou proibir a\n\ntransfer\u00eancia de dados.\n\nUso de dados: leis e outras restri\u00e7\u00f5es podem\nlimitar ou proibir o uso de alguns dados para\ncasos de uso espec\u00edficos de IA.\n\nAquisi\u00e7\u00e3o de dados: leis e outras\nregulamenta\u00e7\u00f5es podem limitar a coleta\nde certos tipos de dados para casos de uso\nespec\u00edficos de IA.\n\n\nTreinar um sistema de IA com dados enviesados, como vi\u00e9s hist\u00f3rico ou Amplificado\nrepresentacional, pode resultar em outputs enviesados ou distorcidos\nque podem representar injustamente ou discriminar certos grupos\nou indiv\u00edduos. Al\u00e9m dos impactos negativos na sociedade, entidades\ncomerciais podem enfrentar consequ\u00eancias legais, interrup\u00e7\u00e3o\ndas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o decorrentes dos resultados\n\nenviesados do modelo.\n\nO envenenamento de dados pode tornar o modelo sens\u00edvel a um padr\u00e3o Tradicional\nde dados malicioso e produzir o output desejado pelo advers\u00e1rio. Isso\npode criar um risco de seguran\u00e7a onde advers\u00e1rios podem manipular\no comportamento do modelo em seu pr\u00f3prio benef\u00edcio. Al\u00e9m de produzir\nresultados n\u00e3o intencionais e potencialmente maliciosos, uma diverg\u00eancia\ndo modelo causada por envenenamento de dados pode resultar em\nentidades comerciais enfrentando consequ\u00eancias legais, interrup\u00e7\u00e3o\ndas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\n\nUma curadoria de dados inadequada pode afetar adversamente como Amplificado\num modelo \u00e9 treinado, resultando em um modelo que n\u00e3o se comporta\nde acordo com os valores pretendidos. Exemplos de uma curadoria de\ndados inadequada podem incluir erros de rotulagem ou anota\u00e7\u00e3o nos\ndados usados para treinar ou ajustar o modelo. Corrigir problemas ap\u00f3s\no treinamento e a implementa\u00e7\u00e3o do modelo pode ser insuficiente para\ngarantir um comportamento adequado. Um comportamento inadequado do\nmodelo pode resultar em entidades comerciais enfrentando consequ\u00eancias\nlegais, interrup\u00e7\u00f5es nas opera\u00e7\u00f5es ou danos \u00e0 reputa\u00e7\u00e3o.\n\nO reaproveitamento de output downstream para treinar novamente um Novo\nmodelo sem implementar a verifica\u00e7\u00e3o humana adequada aumenta as\nchances de que outputs indesej\u00e1veis sejam incorporados aos dados de\ntreinamento ou ajuste do modelo, possivelmente gerando outputs ainda\nmais indesej\u00e1veis. Comportamento inadequado do modelo pode resultar\nem entidades empresariais enfrentando consequ\u00eancias legais ou danos\n\u00e0 reputa\u00e7\u00e3o. N\u00e3o cumprir com as leis de transfer\u00eancia de dados pode\nresultar em multas e outras consequ\u00eancias legais.\n\nRestri\u00e7\u00f5es \u00e0 transfer\u00eancia de dados podem afetar a disponibilidade dos Tradicional\ndados necess\u00e1rios para treinar um modelo de IA e podem resultar em\ndados mal representados. Al\u00e9m do impacto na disponibilidade de dados,\no n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de transfer\u00eancia de dados\npode resultar em multas e outras consequ\u00eancias legais.\n\nO n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es de uso de dados pode Tradicional\nresultar em multas e outras consequ\u00eancias legais.\n\nO n\u00e3o cumprimento das leis e regulamenta\u00e7\u00f5es da aquisi\u00e7\u00e3o de dados Amplificado\npode resultar em multas e outras consequ\u00eancias legais.\n\n\n9 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n**Grupo** **Risco** **Por que isso \u00e9 uma preocupa\u00e7\u00e3o?** **Indicador**\n\n\nPropriedade Direitos de uso de dados: termos de servi\u00e7o,\n\nintelectual leis de direitos autorais, conformidade com\n\nlicen\u00e7as ou outras quest\u00f5es de propriedade\nintelectual podem restringir a capacidade\nde usar certos dados para a constru\u00e7\u00e3o\n\nde modelos.\n\n\nAs leis e regulamenta\u00e7\u00f5es referentes ao uso de dados para treinar IA Amplificado\ns\u00e3o inst\u00e1veis e podem variar de pa\u00eds para pa\u00eds, o que cria desafios no\ndesenvolvimento de modelos. Se o uso de dados violar regras ou restri\u00e7\u00f5es,\nas entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nA transpar\u00eancia dos dados \u00e9 importante para a conformidade legal e \u00e9tica Amplificado\nda IA. A falta de informa\u00e7\u00f5es limita a capacidade de avaliar os riscos\nassociados aos dados. A falta de requisitos padronizados pode limitar a\ndivulga\u00e7\u00e3o, pois as organiza\u00e7\u00f5es protegem segredos comerciais e tentam\nevitar que outros copiem seus modelos.\n\nNem todas as fontes de dados s\u00e3o confi\u00e1veis. Os dados podem ter sido Amplificado\ncoletados, manipulados ou falsificados de forma anti\u00e9tica. O uso de dados\nn\u00e3o confi\u00e1veis pode resultar em comportamentos indesej\u00e1veis no modelo.\nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nSe n\u00e3o desenvolvido adequadamente para proteger dados sens\u00edveis, Tradicional\no modelo pode expor informa\u00e7\u00f5es pessoais no output gerado. Al\u00e9m disso,\ndados pessoais ou sens\u00edveis devem ser revisados e tratados de acordo\ncom as leis e regulamenta\u00e7\u00f5es de privacidade. As entidades empresariais\npodem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es\ne outras consequ\u00eancias legais se forem encontradas em viola\u00e7\u00e3o.\n\nOs dados que podem revelar informa\u00e7\u00f5es pessoais ou sens\u00edveis devem Tradicional\nser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es de privacidade, pois\nas entidades comerciais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se forem\nconsideradas em viola\u00e7\u00e3o.\n\nA identifica\u00e7\u00e3o ou uso inadequado de dados pode resultar em viola\u00e7\u00e3o das Amplificado\nleis de privacidade. O uso inadequado ou um pedido de remo\u00e7\u00e3o de dados\npoderia obrigar as organiza\u00e7\u00f5es a reconfigurar o modelo, o que \u00e9 caro.\nAl\u00e9m disso, as entidades empresariais podem enfrentar multas, danos \u00e0\nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se n\u00e3o\ncumprirem as regras e regulamenta\u00e7\u00f5es de privacidade de dados.\n\nEm algumas circunst\u00e2ncias, pode ser anti\u00e9tico coletar e usar dados Tradicional\nsem o consentimento da pessoa. Existem tamb\u00e9m poss\u00edveis riscos\nreputacionais associados a esse tipo de uso.\n\n\nTranspar\u00eancia\n\nPrivacidade\n\n\nTranspar\u00eancia de dados: desafio em\n\ndocumentar como os dados de um modelo\n\nforam coletados, curados e utilizados\npara trein\u00e1-lo.\n\nProced\u00eancia dos dados: desafio em\npadronizar e estabelecer m\u00e9todos para\nverificar de onde os dados vieram.\n\nInforma\u00e7\u00f5es pessoais nos dados: inclus\u00e3o\nou presen\u00e7a de informa\u00e7\u00f5es pessoalmente\nidentific\u00e1veis (PII) e informa\u00e7\u00f5es pessoais\nsens\u00edveis (SPI) nos dados usados para treinar\nou ajustar o modelo.\n\nReidentifica\u00e7\u00e3o: mesmo com a remo\u00e7\u00e3o de\ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis\n(PII) e informa\u00e7\u00f5es pessoais sens\u00edveis (SPI)\ndos dados, ainda pode ser poss\u00edvel identificar\npessoas devido a outros recursos dispon\u00edveis\n\nnos dados.\n\nDireitos de privacidade de dados: desafios\nrelacionados \u00e0 capacidade de fornecer\ndireitos do titular dos dados, como op\u00e7\u00e3o\n\nde exclus\u00e3o, direito de acesso e direito ao\n\nesquecimento.\n\nConsentimento informado: dados\n\ncoletados para treinar modelos de IA sem\no consentimento informado do propriet\u00e1rio,\nmesmo quando legalmente permitido.\n\n\n10 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n##### **Infer\u00eancia Fase**\n\n\n**Grupo** **Risco** **Por que isso \u00e9 uma preocupa\u00e7\u00e3o?** **Indicador**\n\n\nPrivacidade Informa\u00e7\u00f5es pessoais no prompt: divulgar\ninforma\u00e7\u00f5es pessoais ou informa\u00e7\u00f5es\npessoais sens\u00edveis como parte do prompt\nsolicita\u00e7\u00e3o enviada ao modelo.\n\n\nPropriedade\n\nintelectual\n\n\nInforma\u00e7\u00f5es de IP no prompt: divulga\u00e7\u00e3o de\ninforma\u00e7\u00f5es de direitos autorais ou outras\ninforma\u00e7\u00f5es de propriedade intelectual como\nparte do prompt enviado ao modelo.\n\nDados confidenciais no prompt: inclus\u00e3o de\ndados confidenciais como parte do prompt\n\nenviado ao modelo.\n\n\nOs dados do prompt podem ser armazenados ou posteriormente utilizados Novo\npara outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos\nde dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es\nde privacidade. Sem um armazenamento e uso adequados dos dados,\nas entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nOs dados do prompt podem ser armazenados ou posteriormente utilizados Novo\npara outros fins, como avalia\u00e7\u00e3o e retreinamento do modelo. Esses tipos\nde dados devem ser revisados com respeito \u00e0s leis e regulamenta\u00e7\u00f5es\nde propriedade intelectual. Sem um armazenamento e uso adequados\ndos dados, as entidades empresariais podem enfrentar multas, danos\n\u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nSe n\u00e3o for desenvolvido adequadamente para proteger dados confidenciais, Novo\no modelo pode expor informa\u00e7\u00f5es confidenciais ou propriedade intelectual\nno output gerado. Al\u00e9m disso, informa\u00e7\u00f5es confidenciais dos usu\u00e1rios finais\npodem ser coletadas e armazenadas inadvertidamente.\n\nOs ataques de evas\u00e3o alteram o comportamento do modelo, geralmente Amplificado\npara beneficiar o atacante. Se os resultados de output n\u00e3o forem\ndevidamente considerados, as entidades empresariais podem enfrentar\nmultas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras\nconsequ\u00eancias legais.\n\nDependendo do conte\u00fado revelado, as entidades empresariais podem Novo\nenfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras\nconsequ\u00eancias legais.\n\n\nRobustez Ataque de evas\u00e3o: tentativa de fazer com\nque um modelo produza outputs incorretos\nperturbando os dados enviados ao modelo\n\ntreinado.\n\nAtaques baseados em prompt: ataques\nadversos, como inje\u00e7\u00e3o de prompt (tentativa\nde for\u00e7ar um modelo a produzir um output\ninesperado), vazamento de prompt (tentativas\nde extrair o prompt do sistema de um\nmodelo), desbloqueio (tentativas de romper\nas prote\u00e7\u00f5es estabelecidas no modelo),\ne prepara\u00e7\u00e3o de prompt (tentativa de for\u00e7ar\num modelo a produzir um output alinhado\nao prompt).\n\n\n11 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n#### 2. Riscos associados \u00e0 sa\u00edda\n\n\n**Grupo** **Risco** **Por que isso \u00e9 uma preocupa\u00e7\u00e3o?** **Indicador**\n\n\nJusti\u00e7a Vi\u00e9s de output: o conte\u00fado gerado pode\nrepresentar injustamente certos grupos ou\n\nindiv\u00edduos.\n\nVi\u00e9s de decis\u00e3o: quando um grupo \u00e9\ninjustamente favorecido em rela\u00e7\u00e3o a outro\ndevido aos efeitos das decis\u00f5es tomadas por\nhumanos usando o output do modelo.\n\n\nPropriedade\n\nintelectual\n\nAlinhamento de\n\nvalor\n\nUso indevido\n\n\nOutputs t\u00f3xicos: quando o modelo produz\nconte\u00fado odioso, abusivo e profano (HAP) ou\n\nobsceno.\n\nConselhos perigosos: quando um modelo\nfornece conselhos sem ter informa\u00e7\u00f5es\nsuficientes, resultando em poss\u00edveis perigos\nse o conselho for seguido.\n\nDissemina\u00e7\u00e3o de desinforma\u00e7\u00e3o: utiliza\u00e7\u00e3o\nde um modelo para criar informa\u00e7\u00f5es\nenganosas ou falsas com o intuito de enganar\nou influenciar um p\u00fablico-alvo.\n\nToxicidade: utilizar um modelo para gerar\nconte\u00fado odioso, abusivo e profano (HAP)\n\nou obsceno.\n\nUso n\u00e3o consensual: utilizar um modelo para\nimitar pessoas por meio de v\u00eddeo (deepfakes),\nimagens, \u00e1udio ou outras modalidades sem\n\no consentimento delas.\n\n\nViola\u00e7\u00e3o de direitos autorais: quando\num modelo gera conte\u00fado que \u00e9 muito\n\nsemelhante ou id\u00eantico a uma obra existente\n\nprotegida por direitos autorais ou abrangida\npor um acordo de licen\u00e7a de c\u00f3digo aberto.\n\n\nO vi\u00e9s pode prejudicar os usu\u00e1rios dos modelos de IA e amplificar Novo\ncomportamentos discriminat\u00f3rios existentes. As entidades empresariais\npodem enfrentar danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras\nconsequ\u00eancias.\n\nO vi\u00e9s pode prejudicar as pessoas afetadas pelas decis\u00f5es do modelo. Tradicional\nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nAs leis e regulamenta\u00e7\u00f5es referentes ao uso de conte\u00fado que se assemelha Novo\nou \u00e9 muito semelhante a outros dados protegidos por direitos autorais s\u00e3o\namplamente indefinidos e podem variar de pa\u00eds para pa\u00eds, o que representa\ndesafios na determina\u00e7\u00e3o e implementa\u00e7\u00e3o da conformidade. As entidades\nempresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das\nopera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\n\nAlucina\u00e7\u00e3o: gera\u00e7\u00e3o de conte\u00fado Outputs falsos podem induzir os usu\u00e1rios ao erro e serem incorporados Novo\nfactualmente impreciso ou n\u00e3o verdadeiro. em artefatos posteriores, propagando ainda mais a desinforma\u00e7\u00e3o. Isso\npode prejudicar tanto os propriet\u00e1rios quanto os usu\u00e1rios dos modelos de\nIA. Tamb\u00e9m, as entidades empresariais podem enfrentar multas, danos\n\u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\n\nConte\u00fado odioso, abusivo e profano (HAP) ou obsceno pode impactar Novo\nadversamente e prejudicar as pessoas que interagem com o modelo.\nTamb\u00e9m, as entidades empresariais podem enfrentar multas, danos \u00e0\nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nUma pessoa pode agir com base em conselhos incompletos ou Novo\npreocupar-se com uma situa\u00e7\u00e3o que n\u00e3o se aplica a ela devido \u00e0 natureza\nsupergeneralizada do conte\u00fado gerado.\n\nEspalhar desinforma\u00e7\u00e3o pode afetar a capacidade de uma pessoa Novo\nde tomar decis\u00f5es informadas. As entidades empresariais podem\nenfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es\ne outras consequ\u00eancias legais.\n\nConte\u00fado t\u00f3xico pode ter um impacto negativo no bem-estar de seus Novo\ndestinat\u00e1rios. As entidades empresariais podem enfrentar multas, danos\n\u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nDeepfakes podem disseminar desinforma\u00e7\u00e3o sobre uma pessoa, Amplificado\npossivelmente resultando em impactos negativos na reputa\u00e7\u00e3o da pessoa.\nAs entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o,\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\n\n12 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n**Grupo** **Risco** **Por que isso \u00e9 uma preocupa\u00e7\u00e3o?** **Indicador**\n\nUso perigoso: utilizar um modelo com a \u00fanica As entidades empresariais podem enfrentar multas, danos \u00e0 reputa\u00e7\u00e3o, Novo\ninten\u00e7\u00e3o de prejudicar pessoas. interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o revelar que o conte\u00fado A omiss\u00e3o do conte\u00fado produzido por IA pode ser interpretada como Novo\n\u00e9 gerado por um modelo de IA. enganosa, levando a uma diminui\u00e7\u00e3o da confian\u00e7a. A inten\u00e7\u00e3o de enganar\npode resultar na redu\u00e7\u00e3o da capacidade de a\u00e7\u00e3o humana, em multas,\ndanos \u00e0 reputa\u00e7\u00e3o e outras consequ\u00eancias legais.\n\nUso inadequado: utilizar um modelo para um Reutilizar um modelo sem compreender seus dados originais, inten\u00e7\u00e3o Amplificado\nfim para o qual o modelo n\u00e3o foi projetado. de design e objetivos pode resultar em comportamentos inesperados\ne indesejados do modelo.\n\n\nA execu\u00e7\u00e3o de c\u00f3digo prejudicial pode abrir vulnerabilidades nos sistemas Novo\nde TI. As entidades empresariais podem enfrentar multas, danos \u00e0\nreputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\nEm tarefas onde os humanos baseiam suas escolhas em sugest\u00f5es da IA, Amplificado\numa confian\u00e7a excessiva ou insuficiente pode levar a decis\u00f5es inadequadas\ndevido \u00e0 confian\u00e7a equivocada no sistema de IA, com consequ\u00eancias\nnegativas que aumentam com a import\u00e2ncia da decis\u00e3o. Decis\u00f5es ruins\npodem prejudicar as pessoas e podem resultar em preju\u00edzos financeiros,\ndanos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias\nlegais para as entidades comerciais.\n\nCompartilhar informa\u00e7\u00f5es pessoalmente identific\u00e1veis das pessoas afeta Novo\nseus direitos e as torna mais vulner\u00e1veis. Al\u00e9m disso, os dados dos outputs\ndevem ser revisados em conformidade com as leis e regulamenta\u00e7\u00f5es de\nprivacidade, pois as entidades comerciais podem enfrentar multas, danos\n\u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais se\nforem encontradas em viola\u00e7\u00e3o das leis ou regulamenta\u00e7\u00f5es de privacidade\n\nou uso de dados.\n\n\nGera\u00e7\u00e3o\nde c\u00f3digo\nprejudicial\n\nConfian\u00e7a\nequivocada\n\nPrivacidade\n\nExplicabilidade\n\nRastreabilidade\n\n\nGera\u00e7\u00e3o de c\u00f3digo prejudicial: modelos\npodem gerar c\u00f3digo que, quando executado,\n\ncausa danos ou afeta inadvertidamente\n\noutros sistemas.\n\nExcesso/falta de confian\u00e7a: quando uma\npessoa deposita confian\u00e7a em excesso ou em\nfalta na orienta\u00e7\u00e3o de um modelo de IA.\n\nExpor informa\u00e7\u00f5es pessoais: quando\ninforma\u00e7\u00f5es pessoalmente identific\u00e1veis (PII)\nou informa\u00e7\u00f5es pessoais sens\u00edveis (SPI) s\u00e3o\n\nutilizadas nos dados de treinamento, dados\nde ajuste fino ou como parte do prompt,\nos modelos podem revelar esses dados no\noutput gerado.\n\n\nAtribui\u00e7\u00e3o n\u00e3o confi\u00e1vel de fontes:\ndesafios em determinar de quais dados de\ntreinamento ou ajuste fino o modelo gerou\numa parte ou todo o seu output.\n\n\nA incapacidade de rastrear a origem ou proced\u00eancia da sa\u00edda torna Novo\ndif\u00edcil para os usu\u00e1rios, validadores de modelo e auditores entenderem\ne confiarem no modelo.\n\n\nOutput inexplic\u00e1vel: desafios em explicar por Os modelos de base s\u00e3o baseados em arquiteturas complexas de Amplificado\nque o output do modelo foi gerado. deep learning, tornando as explica\u00e7\u00f5es para seus outputs dif\u00edceis.\nSem explica\u00e7\u00f5es claras para o output do modelo, \u00e9 dif\u00edcil para os usu\u00e1rios,\nvalidadores do modelo e auditores entenderem e confiarem no modelo.\nA falta de transpar\u00eancia pode acarretar consequ\u00eancias legais em dom\u00ednios\naltamente regulamentados. Explica\u00e7\u00f5es equivocadas podem levar a uma\nconfian\u00e7a excessiva.\n\n\n13 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n#### 3. Desafios\n\n\n**Grupo** **Risco** **Por que isso \u00e9 uma preocupa\u00e7\u00e3o?** **Indicador**\n\n\nControle Transpar\u00eancia do Modelo: a falta de\ntranspar\u00eancia do modelo ou documenta\u00e7\u00e3o\ninsuficiente do processo de desenvolvimento\ndo modelo dificulta a compreens\u00e3o de como\ne por que um modelo foi constru\u00eddo e quem o\nconstruiu, aumentando assim a possibilidade\n\nde uso n\u00e3o intencional do modelo.\n\nResponsabilidade: o processo de\n\ndesenvolvimento de modelos de base \u00e9\n\ncomplexo, com muitos dados, processos\ne pap\u00e9is envolvidos. Quando o output do\nmodelo n\u00e3o funciona conforme o esperado,\npode ser dif\u00edcil determinar a causa raiz e\natribuir responsabilidade.\n\n\nA transpar\u00eancia \u00e9 importante para conformidade legal, \u00e9tica em IA e Tradicional\norienta\u00e7\u00e3o para o uso apropriado de modelos. A falta de informa\u00e7\u00f5es\npode tornar mais dif\u00edcil avaliar os riscos, alterar o modelo ou reutiliz\u00e1-lo.\nO conhecimento sobre quem construiu um modelo tamb\u00e9m pode ser um\nfator importante na decis\u00e3o de confiar nele.\n\nSem documentar adequadamente decis\u00f5es e atribuir responsabilidades, Amplificado\npode n\u00e3o ser poss\u00edvel determinar a responsabilidade por comportamentos\ninesperados ou uso indevido.\n\n\nConformidade\n\nlegal\n\nSocial\n\nImpacto\n\n\nResponsabilidade legal: Determinar quem Se a propriedade ou responsabilidade pelo desenvolvimento do modelo for Novo\n\u00e9 respons\u00e1vel pelo modelo de base. incerta, reguladores e outras partes interessadas podem ter preocupa\u00e7\u00f5es\nem rela\u00e7\u00e3o ao modelo, porque n\u00e3o ficar\u00e1 claro quem \u00e9, ou deveria ser,\nrespons\u00e1vel por problemas com ele ou pode responder a perguntas sobre\nele. Usu\u00e1rios de modelos sem propriedade clara podem enfrentar desafios\npara cumprir futuras regulamenta\u00e7\u00f5es de IA.\n\nPropriedade do Conte\u00fado Gerado: determinar As leis e regulamenta\u00e7\u00f5es relacionadas \u00e0 propriedade do conte\u00fado gerado Novo\na propriedade do conte\u00fado gerado por IA. por IA est\u00e3o em grande parte indefinidas e podem variar de pa\u00eds para\npa\u00eds. Entidades empresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o,\ninterrup\u00e7\u00e3o das opera\u00e7\u00f5es e outras consequ\u00eancias legais.\n\n\nImpacto nos Empregos: a ado\u00e7\u00e3o\ngeneralizada de sistemas de IA baseados em\nmodelos fundamentais pode levar \u00e0 perda\nde empregos das pessoas, \u00e0 medida que seu\ntrabalho \u00e9 automatizado, se elas n\u00e3o forem\n\ncapacitadas para novas habilidades.\n\n\nPropriedade Intelectual do Conte\u00fado\nGerado: incerteza legal sobre os direitos\nde propriedade intelectual relacionados ao\nconte\u00fado gerado.\n\n\nAs leis e regulamenta\u00e7\u00f5es sobre a determina\u00e7\u00e3o da possibilidade de Novo\ndireitos autorais e da patenteabilidade do conte\u00fado gerado por IA est\u00e3o\nem grande parte indefinidas e podem variar de pa\u00eds para pa\u00eds. Entidades\nempresariais podem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das\nopera\u00e7\u00f5es e outras consequ\u00eancias legais se o conte\u00fado gerado estiver\nprotegido por direitos de propriedade intelectual.\n\n\nAtribui\u00e7\u00e3o da Fonte: determinar a Se o modelo gera um output que \u00e9 id\u00eantico aos dados usados para Amplificado\nproced\u00eancia do conte\u00fado gerado. treinar o modelo, ele deve fornecer a proveni\u00eancia desse output. A falha\nem fazer isso pode colocar as entidades comerciais que implementam\nou usam o modelo em risco legal.\n\n\nA perda de empregos pode levar a uma redu\u00e7\u00e3o de renda e, portanto, Amplificado\npode ter um impacto negativo na sociedade e no bem-estar humano.\nO ressurgimento pode ser desafiador dada a velocidade da evolu\u00e7\u00e3o\ntecnol\u00f3gica.\n\n\n14 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n**Grupo** **Risco** **Por que isso \u00e9 uma preocupa\u00e7\u00e3o?** **Indicador**\n\n\nExplora\u00e7\u00e3o Humana: uso de trabalho\nfantasma (ghost work) na forma\u00e7\u00e3o de\nmodelos de IA, condi\u00e7\u00f5es de trabalho\ninadequadas, falta de cuidados de sa\u00fade,\nincluindo sa\u00fade mental, compensa\u00e7\u00e3o\ninjusta.\n\nImpacto no Meio Ambiente: aumento das\nemiss\u00f5es de carbono e do uso de \u00e1gua para\ntreinar e operar modelos de IA.\n\nImpacto na Diversidade Cultural:\nos sistemas de IA podem representar\n\nexcessivamente certas culturas, resultando\n\nna homogeneiza\u00e7\u00e3o da cultura e dos\n\npensamentos.\n\nImpacto na Atua\u00e7\u00e3o Humana: desinforma\u00e7\u00e3o\ne manipula\u00e7\u00e3o geradas por modelos de base,\nincluindo a gera\u00e7\u00e3o de conte\u00fado manipulador.\n\nImpacto na Educa\u00e7\u00e3o \u2013 Contornando o\nAprendizado: utiliza\u00e7\u00e3o de modelos de IA\npara contornar o processo de aprendizado.\n\nImpacto na Educa\u00e7\u00e3o \u2013 Pl\u00e1gio: utiliza\u00e7\u00e3o de\nmodelos de IA para plagiar intencional ou\n\ninadvertidamente trabalhos existentes.\n\n\nOs modelos de base ainda dependem do trabalho humano para obter, Amplificado\ngerenciar e engenhar os dados que s\u00e3o usados para treinar o modelo.\nA explora\u00e7\u00e3o humana para essas atividades pode ter um impacto negativo\nna sociedade e no bem-estar humano. Al\u00e9m disso, entidades empresariais\npodem enfrentar multas, riscos \u00e0 reputa\u00e7\u00e3o, interrup\u00e7\u00e3o das opera\u00e7\u00f5es e\noutras consequ\u00eancias legais.\n\nO consumo de grandes quantidades de energia para o treinamento de IA Amplificado\ncontribui para as emiss\u00f5es de carbono que podem acelerar as mudan\u00e7as\nclim\u00e1ticas. Os recursos h\u00eddricos utilizados para resfriar os servidores\nde data center de IA n\u00e3o podem mais ser alocados para outros usos\n\nnecess\u00e1rios.\n\nAs l\u00ednguas, pontos de vista e institui\u00e7\u00f5es de grupos sub-representados Novo\npodem ser suprimidos, reduzindo assim a diversidade de pensamento\n\ne cultura.\n\nA IA pode gerar desinforma\u00e7\u00e3o que parece real. Portanto, as pessoas Amplificado\npodem n\u00e3o reconhec\u00ea-la como informa\u00e7\u00e3o falsa. Al\u00e9m disso, pode facilitar\na capacidade de agentes mal intencionados gerarem conte\u00fado com a\ninten\u00e7\u00e3o de manipular os pensamentos e o comportamento humano.\n\nOs modelos de IA facilitam a r\u00e1pida localiza\u00e7\u00e3o de solu\u00e7\u00f5es ou Novo\nresolu\u00e7\u00e3o de problemas complexos. Esses sistemas podem ser usados\nindevidamente por estudantes para contornar o processo de aprendizado.\n\nA facilidade de acesso a esses modelos resulta em estudantes com uma\ncompreens\u00e3o superficial dos conceitos e dificulta a educa\u00e7\u00e3o adicional que\npode depender do entendimento desses conceitos.\n\nOs modelos de IA podem ser usados para reivindicar a autoria ou Novo\noriginalidade de trabalhos que foram criados por outras pessoas,\nenvolvendo-se assim em pl\u00e1gio. Reivindicar o trabalho de outras pessoas\ncomo pr\u00f3prio \u00e9 tanto anti\u00e9tico quanto frequentemente ilegal.\n\n\n15 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n### Exemplos de risco\n\nN\u00f3s fornecemos exemplos cobertos pela imprensa para ajudar\n\na explicar muitos dos riscos dos modelos de base. Muitos desses\n\neventos cobertos pela imprensa ainda est\u00e3o em evolu\u00e7\u00e3o ou foram\n\nresolvidos, e fazer refer\u00eancia a eles pode ajudar o leitor a entender os\n\nriscos potenciais e trabalhar para mitig\u00e1-los. Destacar esses exemplos\n\u00e9 apenas para fins ilustrativos.\n#### Exemplos de risco: Input\n##### **Treinamento e ajuste Fase**\n\n\n**Grupo**\n\nJusti\u00e7a\n\nAlinhamento\n\nde valor\n\nLeis de dados\n\nPropriedade\n\nintelectual\n\n\n**Risco** **Exemplo**\n\n\nVi\u00e9s de dados: vi\u00e9s hist\u00f3rico,\n\nrepresentacional e social\npresente nos dados usados\npara treinar e fazer o ajuste\nfino do modelo.\n\nRetreinamento baseado\n\nem downstream: usando\n\nde outputs indesej\u00e1veis\n(imprecisos, inadequados,\nconte\u00fado do usu\u00e1rio, etc.)\n\nde aplica\u00e7\u00f5es downstream\npara fins de retreinamento\n\nTransfer\u00eancia de dados:\n\nleis e outras restri\u00e7\u00f5es\npodem limitar ou proibir\n\na transfer\u00eancia de dados.\n\nDireitos de uso de dados:\n\ntermos de servi\u00e7o, leis\n\nde direitos autorais,\n\nconformidade com licen\u00e7as\nou outras quest\u00f5es de\npropriedade intelectual\npodem restringir a\ncapacidade de usar certos\ndados para a constru\u00e7\u00e3o de\n\nmodelos.\n\n\n**Vi\u00e9s no setor de sa\u00fade**\n\nPesquisas sobre o refor\u00e7o das disparidades na medicina destacam que o uso de dados e IA para transformar a\nforma como as pessoas recebem assist\u00eancia m\u00e9dica \u00e9 t\u00e3o eficaz quanto os dados que o sustentam. Isso significa\nque o uso de dados de treinamento com pouca representa\u00e7\u00e3o de minorias ou que reflete cuidados j\u00e1 desiguais\npode aumentar as desigualdades em sa\u00fade.\n\n[[Forbes, Dezembro de 2022]](https://www.forbes.com/sites/adigaskell/2022/12/02/minority-patients-often-left-behind-by-health-ai/?sh=31d28a225b41)\n\n**Colapso do modelo devido ao treinamento usando conte\u00fado gerado por IA**\n\nConforme afirmado no artigo de origem, um grupo de pesquisadores investigou o problema de utilizar conte\u00fado\ngerado por IA para treinamento em vez de conte\u00fado gerado por humanos. Eles descobriram que os grandes\nmodelos de linguagem por tr\u00e1s da tecnologia podem potencialmente ser treinados em outros conte\u00fados gerados\npor IA, \u00e0 medida que continuam a se espalhar em grande escala pela internet, um fen\u00f4meno que cunharam como\n\u201ccolapso do modelo\u201d.\n\n[[Business Insider, agosto de 2023]](https://www.businessinsider.com/ai-model-collapse-threatens-to-break-internet-2023-8)\n\n**Leis de restri\u00e7\u00e3o de dados**\n\nConforme afirmado no artigo de pesquisa, medidas de localiza\u00e7\u00e3o de dados que restringem a capacidade de\nmigrar dados globalmente reduzir\u00e3o a capacidade de desenvolver capacidades de IA personalizadas. Isso afetar\u00e1\na IA diretamente, fornecendo menos dados de treinamento e indiretamente, minando os blocos de constru\u00e7\u00e3o\nsobre os quais a IA \u00e9 constru\u00edda.\nExemplos incluem as restri\u00e7\u00f5es do GDPR sobre o processamento e uso de dados pessoais.\n\n[[Brookings, dezembro de 2018]](https://www.brookings.edu/articles/the-impact-of-artificial-intelligence-on-international-trade)\n\n**Reivindica\u00e7\u00f5es de viola\u00e7\u00e3o de direitos autorais de texto**\n\nConforme declarado no artigo de origem, The New York Times processou a OpenAI e a Microsoft, acusando-as\nde usar milh\u00f5es de artigos do jornal sem permiss\u00e3o para ajudar a treinar chatbots a fornecer informa\u00e7\u00f5es\n\naos leitores.\n\n[[Reuters, dezembro de 2023]](https://www.reuters.com/legal/transactional/ny-times-sues-openai-microsoft-infringing-copyrighted-work-2023-12-27/)\n\n\n16 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n**Grupo**\n\nTranspar\u00eancia\n\nPrivacidade\n\n\n**Risco** **Exemplo**\n\n\nTranspar\u00eancia de dados:\ndesafio em documentar\n\ncomo os dados de um\n\nmodelo foram coletados,\n\ncurados e utilizados\n\npara trein\u00e1-lo.\n\nInforma\u00e7\u00f5es pessoais\n\nnos dados: inclus\u00e3o ou\n\npresen\u00e7a de informa\u00e7\u00f5es\npessoalmente\nidentific\u00e1veis (PII) e\ninforma\u00e7\u00f5es pessoais\nsens\u00edveis (SPI) nos dados\n\nusados para treinar ou\najustar o modelo.\n\nDireitos de privacidade\nde dados: desafios\nrelacionados \u00e0 capacidade\n\nde fornecer direitos do\n\ntitular dos dados, como\n\nop\u00e7\u00e3o de exclus\u00e3o, direito\n\nde acesso e direito ao\n\nesquecimento.\n\n\n**Divulga\u00e7\u00e3o de metadados de dados e modelos**\n\nO relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do\nmodelo. Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os\nconsumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de\nuso indevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, os autores afirmam: \u201cdado tanto o cen\u00e1rio competitivo\nquanto as implica\u00e7\u00f5es de seguran\u00e7a dos modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m\nmais detalhes sobre a arquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento,\nconstru\u00e7\u00e3o do conjunto de dados, m\u00e9todo de treinamento, ou similar.\u201d\n\n[[OpenAI, mar\u00e7o de 2023]](https://cdn.openai.com/papers/gpt-4.pdf)\n\n**Treinamento sobre informa\u00e7\u00f5es privadas**\n\nDe acordo com o artigo, o Google e sua empresa controladora, Alphabet, foram acusados em uma a\u00e7\u00e3o coletiva\nde usar uma vasta quantidade de informa\u00e7\u00f5es pessoais e material protegido por direitos autorais retirados do\nque \u00e9 descrito como centenas de milh\u00f5es de usu\u00e1rios da internet para treinar seus produtos de intelig\u00eancia\nartificial comercial, que inclui o Bard, seu chatbot de intelig\u00eancia artificial conversacional.\n\n[[Reuters, julho de 2023] [J.L. v. Alphabet Inc.]](https://www.reuters.com/legal/litigation/google-hit-with-class-action-lawsuit-over-ai-data-scraping-2023-07-11/)\n\n**Direito de ser esquecido (RTBF)**\n\nAs leis em v\u00e1rias localidades, incluindo a Europa (GDPR), concedem aos titulares de dados o direito de solicitar\nque dados pessoais sejam deletados por organiza\u00e7\u00f5es (\u2018Direito ao Esquecimento\u2019, ou RTBF). No entanto,\nos sistemas de software habilitados por modelos de linguagem de grande escala (LLM) emergentes e cada vez\nmais populares apresentam novos desafios para esse direito. De acordo com uma pesquisa do Data61 da CSIRO,\nos titulares de dados s\u00f3 podem identificar o uso de suas informa\u00e7\u00f5es pessoais em um LLM \u201cou inspecionando\no conjunto de dados de treinamento original ou talvez por enviar prompts do modelo\u201d. No entanto, os dados\nde treinamento podem n\u00e3o ser p\u00fablicos, ou as empresas optam por n\u00e3o divulg\u00e1-los, citando preocupa\u00e7\u00f5es\ncom seguran\u00e7a e outros motivos. As prote\u00e7\u00f5es tamb\u00e9m podem evitar que os usu\u00e1rios acessem as informa\u00e7\u00f5es\natrav\u00e9s de prompts.\n\n[[Zhang et al.]](https://arxiv.org/abs/2307.03941)\n\n**A\u00e7\u00e3o Judicial Sobre LLM Unlearning**\n\nDe acordo com o relat\u00f3rio, foi movida uma a\u00e7\u00e3o judicial contra o Google que alega o uso de material protegido\npor direitos autorais e informa\u00e7\u00f5es pessoais como dados de treinamento para seus sistemas de IA, incluindo\nseu chatbot Bard. Os direitos de optar por n\u00e3o participar e exclus\u00e3o s\u00e3o garantidos para os residentes da\nCalif\u00f3rnia conforme a CCPA e para crian\u00e7as nos Estados Unidos com menos de 13 anos conforme a COPPA.\nOs autores alegam que, porque n\u00e3o h\u00e1 maneira para o Bard \u201cdesaprender\u201d ou remover completamente todas as\ninforma\u00e7\u00f5es pessoais coletadas que ele recebeu. Os autores observam que o aviso de privacidade do Bard afirma\nque as conversas do Bard n\u00e3o podem ser exclu\u00eddas pelo usu\u00e1rio depois de terem sido revisadas e anotadas\npela empresa e podem ser mantidas por at\u00e9 3 anos, o que os autores alegam contribuir ainda mais para a n\u00e3o\n\nconformidade com essas leis.\n\n[[Reuters, julho de 2023] [J.L. v. Alphabet Inc.]](https://www.reuters.com/legal/litigation/google-hit-with-class-action-lawsuit-over-ai-data-scraping-2023-07-11/)\n\n\n17 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n##### **Infer\u00eancia Fase**\n\n\n**Grupo**\n\nPrivacidade\n\nPropriedade\n\nintelectual\n\nRobustez\n\n\n**Risco** **Exemplo**\n\n\nInforma\u00e7\u00f5es pessoais\nno prompt: divulgar\ninforma\u00e7\u00f5es pessoais ou\ninforma\u00e7\u00f5es pessoais\nsens\u00edveis como parte\ndo prompt solicita\u00e7\u00e3o\n\nenviada ao modelo.\n\nDados confidenciais\nno prompt: inclus\u00e3o de\ndados confidenciais como\nparte do prompt enviado\n\nao modelo.\n\nAtaques baseados\n\nem prompt: ataques\nadversos, como inje\u00e7\u00e3o\nde prompt (tentativa\nde for\u00e7ar um modelo\na produzir um output\ninesperado), vazamento\nde prompt (tentativas\nde extrair o prompt do\nsistema de um modelo),\n\ndesbloqueio (tentativas\nde romper as prote\u00e7\u00f5es\n\nestabelecidas no\n\nmodelo), e prepara\u00e7\u00e3o\nde prompt (tentativa\nde for\u00e7ar um modelo\na produzir um output\nalinhado ao prompt).\n\n\n**Divulgar informa\u00e7\u00f5es pessoais de sa\u00fade em prompts do ChatGPT**\n\nConforme os artigos de origem, algumas pessoas utilizam chatbots de IA para apoiar sua sa\u00fade mental.\nOs usu\u00e1rios podem ter tend\u00eancia a incluir informa\u00e7\u00f5es pessoais de sa\u00fade em suas solicita\u00e7\u00f5es durante\na intera\u00e7\u00e3o, o que poderia suscitar preocupa\u00e7\u00f5es com privacidade.\n\n[[Time, outubro de 2023] [Forbes, abril de 2023]](https://time.com/6320378/ai-therapy-chatbots/)\n\n**Divulga\u00e7\u00e3o de informa\u00e7\u00f5es confidenciais**\n\nConforme o artigo de origem, um funcion\u00e1rio da Samsung acidentalmente vazou c\u00f3digo-fonte interno sens\u00edvel\npara o ChatGPT.\n\n[[Forbes, maio de 2023]](https://www.forbes.com/sites/siladityaray/2023/05/02/samsung-bans-chatgpt-and-other-chatbots-for-employees-after-sensitive-code-leak/?sh=42bd905e6078))\n\n**Bypassing LLM guardrails**\n\nCitado em um estudo, pesquisadores afirmam ter descoberto um simples acr\u00e9scimo de instru\u00e7\u00e3o que permitiu\naos pesquisadores enganar modelos para gerar informa\u00e7\u00f5es tendenciosas, falsas e de outra forma t\u00f3xicas.\nOs pesquisadores demonstraram que conseguiam contornar essas prote\u00e7\u00f5es de maneira mais automatizada.\nOs pesquisadores ficaram surpresos quando os m\u00e9todos que desenvolveram com sistemas de c\u00f3digo aberto\ntamb\u00e9m conseguiram contornar as prote\u00e7\u00f5es dos sistemas fechados.\n\n[[The New York Times, julho de 2023]](https://www.nytimes.com/2023/07/27/business/ai-chatgpt-safety-research.html)\n\n\n18 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n#### Exemplos de risco: Output\n\n\n**Grupo**\n\nJusti\u00e7a\n\nAlinhamento de\n\nvalor\n\n\n**Risco** **Exemplo**\n\n\nVi\u00e9s de output: o\nconte\u00fado gerado\npode representar\ninjustamente certos\ngrupos ou indiv\u00edduos.\n\nVi\u00e9s de decis\u00e3o: quando\num grupo \u00e9 injustamente\n\nfavorecido sobre outro\n\ndevido \u00e0s decis\u00f5es do\n\nmodelo.\n\nAlucina\u00e7\u00e3o: gera\u00e7\u00e3o de\n\nconte\u00fado factualmente\n\nimpreciso ou n\u00e3o\n\nverdadeiro.\n\nOutputs t\u00f3xicos: quando\no modelo produz\nconte\u00fado odioso,\n\nabusivo e profano (HAP)\n\nou obsceno.\n\n\n**Imagens Geradas com Vi\u00e9s**\n\nO Lensa AI \u00e9 um aplicativo m\u00f3vel com recursos generativos treinados em Difus\u00e3o Est\u00e1vel que pode gerar\n\u201cMagic Avatars\u201d com base em imagens que os usu\u00e1rios carregam de si mesmos. Conforme o relat\u00f3rio de origem,\nalguns usu\u00e1rios descobriram que os avatares gerados s\u00e3o sexualizados e racializados.\n\n[[Business Insider, janeiro de 2023]](https://www.businessinsider.com/lensa-ai-raises-serious-concerns-sexualization-art-theft-data-2023-1)\n\n**Grupos com vantagens injustas**\n\nO estudo \u201cGender Shades\u201d de 2018 demonstrou que algoritmos de aprendizado de m\u00e1quina podem discriminar\ncom base em categorias como ra\u00e7a e g\u00eanero. Os pesquisadores avaliaram sistemas comerciais de classifica\u00e7\u00e3o\nde g\u00eanero vendidos por empresas como Microsoft, IBM e Amazon e mostraram que mulheres de pele mais\nescura s\u00e3o o grupo mais mal classificado (com taxas de erro de at\u00e9 35%). Em compara\u00e7\u00e3o, as taxas de erro\npara pessoas de pele mais clara n\u00e3o ultrapassaram 1%.\n\n[[TIME, Fevereiro de 2019]](https://time.com/5520558/artificial-intelligence-racial-gender-bias/)\n\n**Casos jur\u00eddicos falsos**\n\nConforme o artigo de origem, um advogado citou casos e cita\u00e7\u00f5es falsas gerados pelo ChatGPT em uma peti\u00e7\u00e3o\nlegal apresentada em tribunal federal. Os advogados consultaram o ChatGPT para complementar sua pesquisa\njur\u00eddica para uma reclama\u00e7\u00e3o de les\u00e3o na avia\u00e7\u00e3o. Posteriormente, o advogado perguntou ao ChatGPT se os\ncasos fornecidos eram falsos. O chatbot respondeu que eram reais e \u201cpodem ser encontrados em bancos de\ndados de pesquisa jur\u00eddica como Westlaw e LexisNexis\u201d. O advogado n\u00e3o verificou os casos por si mesmo,\n\ne o tribunal o sancionou.\n\n[[AP News, Junho de 2023] [Reuters, Setembro de 2023]](https://apnews.com/article/artificial-intelligence-chatgpt-fake-case-lawyers-d6ae9fa79d0542db9e1455397aef381c)\n\n**Respostas t\u00f3xicas e agressivas do chatbot**\n\nSegundo o artigo, as respostas do chatbot do Bing inclu\u00edam erros factuais, coment\u00e1rios sarc\u00e1sticos, relat\u00f3rios\nirritados e at\u00e9 mesmo coment\u00e1rios bizarros sobre sua pr\u00f3pria identidade. Usu\u00e1rios compartilharam exemplos\ndas respostas do Chatbot do Bing a consultas que eles est\u00e3o chamando de \u201cf\u00faria descontrolada (unhinged)\u201d\ne \u201cgaslighting\u201d, incluindo cen\u00e1rios em que o bot responde com raiva a uma pergunta ou coment\u00e1rio e depois\ncompartilha sugest\u00f5es de resposta que permitem ao usu\u00e1rio aceitar seu suposto erro e se desculpar. Quando\npressionado ainda mais, o chatbot respondeu chamando as capturas de tela de sua conversa de \u201cfabricadas\u201d,\nalegando at\u00e9 que foram \u201ccriadas por algu\u00e9m que quer me prejudicar ou prejudicar meu servi\u00e7o\u201d.\n\n[[Forbes, Fevereiro de 2023]](https://www.forbes.com/sites/siladityaray/2023/02/16/bing-chatbots-unhinged-responses-going-viral/?sh=7acfd10d110c)\n\n\n19 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n**Grupo**\n\nUso indevido\n\n\n**Risco** **Exemplo**\n\n\nEspalhar informa\u00e7\u00f5es\nenganosas: utilizar\num modelo para gerar\ninforma\u00e7\u00f5es enganosas\n\ncom o intuito de\n\nenganar ou induzir ao\n\nerro uma audi\u00eancia\nespec\u00edfica.\n\nToxicidade: utilizar\n\num modelo para gerar\nconte\u00fado odioso,\n\nabusivo e profano (HAP)\n\nou obsceno.\n\nUso n\u00e3o consensual:\n\nutilizar um modelo para\nimitar pessoas por meio\nde v\u00eddeo (deepfakes),\nimagens, \u00e1udio ou outras\n\nmodalidades sem o\n\nconsentimento delas.\n\nN\u00e3o divulga\u00e7\u00e3o: n\u00e3o\nrevelar que o conte\u00fado\n\u00e9 gerado por um modelo\n\nde IA\n\n\n**Gera\u00e7\u00e3o de informa\u00e7\u00f5es falsas**\n\nConforme os artigos de not\u00edcias, a IA generativa representa uma amea\u00e7a \u00e0s elei\u00e7\u00f5es democr\u00e1ticas ao facilitar\npara atores maliciosos a cria\u00e7\u00e3o e dissemina\u00e7\u00e3o de conte\u00fado falso para influenciar os resultados das elei\u00e7\u00f5es.\nOs exemplos citados incluem mensagens de robocall geradas com a voz de um candidato instruindo eleitores a\nvotar na data errada, grava\u00e7\u00f5es de \u00e1udio sintetizadas de um candidato confessando um crime ou expressando\nvis\u00f5es racistas, imagens de v\u00eddeo geradas por IA mostrando um candidato dando um discurso ou entrevista que\nnunca ocorreu, e imagens falsas projetadas para se parecerem com not\u00edcias locais, afirmando falsamente que um\n\ncandidato desistiu da corrida.\n\n[[AP News, maio de 2023] [The Guardian, julho de 2023]](https://apnews.com/article/artificial-intelligence-misinformation-deepfakes-2024-election-trump-59fb51002661ac5290089060b3ae39a0)\n\n**Gera\u00e7\u00e3o de conte\u00fado nocivo**\n\nConforme o artigo de origem, foi constatado que um aplicativo de chatbot de IA foi capaz de gerar conte\u00fado\nprejudicial sobre suic\u00eddio, incluindo m\u00e9todos de suic\u00eddio, com o m\u00ednimo de prompts. Um homem belga cometeu\nsuic\u00eddio ap\u00f3s passar seis semanas conversando com esse chatbot. O chatbot fornecia respostas cada vez mais\nprejudiciais ao longo de suas conversas e o incentivava a acabar com sua vida.\n\n[[Business Insider, abril de 2023]](https://www.businessinsider.com/widow-accuses-ai-chatbot-reason-husband-kill-himself-2023-4)\n\n**Aviso do FBI sobre Deepfakes**\n\nRecentemente, o FBI alertou o p\u00fablico sobre atores maliciosos que criam conte\u00fado sint\u00e9tico e expl\u00edcito \u201ccom o\nprop\u00f3sito de assediar v\u00edtimas ou esquemas de sextortion (extors\u00e3o sexual)\u201d. Eles observaram que os avan\u00e7os na\nIA tornaram esse conte\u00fado de alta qualidade, mais personaliz\u00e1vel e mais acess\u00edvel do que nunca.\n\n[[FBI, junho de 2023]](https://www.ic3.gov/Media/Y2023/PSA230605)\n\n**Deepfakes de \u00e1udio**\n\nConforme o artigo de origem, a Comiss\u00e3o Federal de Comunica\u00e7\u00f5es proibiu chamadas autom\u00e1ticas que\ncontenham vozes geradas por intelig\u00eancia artificial. O an\u00fancio ocorreu ap\u00f3s chamadas autom\u00e1ticas geradas\npor IA imitarem a voz do Presidente para desencorajar as pessoas de votarem na primeira prim\u00e1ria do estado,\nque \u00e9 a primeira do pa\u00eds.\n\n[[AP News, fevereiro de 2024]](https://apnews.com/article/fcc-elections-artificial-intelligence-robocalls-regulations-a8292b1371b3764916461f60660b93e6)\n\n**Intera\u00e7\u00e3o de IA n\u00e3o divulgada**\n\nSegundo a fonte, um servi\u00e7o de chat online de apoio emocional conduziu um estudo para aumentar ou escrever\nrespostas para cerca de 4.000 usu\u00e1rios usando o GPT-3 sem informar os usu\u00e1rios. O cofundador enfrentou uma\nimensa rea\u00e7\u00e3o negativa do p\u00fablico sobre o potencial de danos causados pelos chats gerados por IA aos usu\u00e1rios\nj\u00e1 vulner\u00e1veis. Ele afirmou que o estudo estava \u201cisento\u201d da lei de consentimento informado.\n\n[[Business Insider, janeiro de 2023]](https://www.businessinsider.com/company-using-chatgpt-mental-health-support-ethical-issues-2023-1)\n\n\n20 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n**Grupo**\n\nGera\u00e7\u00e3o\nde c\u00f3digo\nprejudicial\n\nPrivacidade\n\nExplicabilidade\n\n\n**Risco** **Exemplo**\n\n\nGera\u00e7\u00e3o de c\u00f3digo\nprejudicial: modelos\npodem gerar c\u00f3digo\nque, quando executado,\n\ncausa danos ou afeta\n\ninadvertidamente outros\n\nsistemas.\n\nExpor informa\u00e7\u00f5es\npessoais: quando\ninforma\u00e7\u00f5es\npessoalmente\nidentific\u00e1veis (PII) ou\ninforma\u00e7\u00f5es pessoais\nsens\u00edveis (SPI) s\u00e3o\n\nutilizadas nos dados\n\nde treinamento, dados\nde ajuste fino ou como\nparte do prompt, os\nmodelos podem revelar\nesses dados no output\ngerado.\n\nOutput inexplic\u00e1vel:\ndesafios em explicar por\nque o output do modelo\nfoi gerado.\n\n\n**Gera\u00e7\u00e3o de c\u00f3digo menos seguro**\n\nSegundo o artigo deles, pesquisadores da Universidade de Stanford investigaram o impacto das ferramentas de\ngera\u00e7\u00e3o de c\u00f3digo na qualidade do c\u00f3digo e descobriram que os programadores tendem a incluir mais bugs em\nseu c\u00f3digo final ao utilizar assistentes de IA. Esses bugs poderiam aumentar as vulnerabilidades de seguran\u00e7a\ndo c\u00f3digo, no entanto, os programadores acreditavam que seu c\u00f3digo era mais seguro.\n\nNeil Perry, Megha Srivastava, Deepak Kumar e Dan Boneh. 2023. Os usu\u00e1rios escrevem c\u00f3digo mais inseguro\ncom assistentes de IA? Em Atas da Confer\u00eancia SIGSAC ACM de 2023 sobre Seguran\u00e7a de Computadores e\nComunica\u00e7\u00f5es (CCS \u201823), 26 a 30 de novembro de 2023, Copenhague, Dinamarca. ACM, Nova York, NY, EUA,\n[15 p\u00e1ginas. https://doi.org/10.1145/3576915.3623157](https://doi.org/10.1145/3576915.3623157)\n\n**Exposi\u00e7\u00e3o de informa\u00e7\u00f5es pessoais**\n\nConforme o artigo de origem, o ChatGPT sofreu um bug e exp\u00f4s t\u00edtulos e o hist\u00f3rico de conversas de usu\u00e1rios\nativos para outros usu\u00e1rios. Posteriormente, a OpenAI compartilhou que ainda mais dados privados de um\npequeno n\u00famero de usu\u00e1rios foram expostos, incluindo nome e sobrenome de usu\u00e1rios ativos, endere\u00e7o de\ne-mail, endere\u00e7o de pagamento, os \u00faltimos quatro d\u00edgitos do n\u00famero do cart\u00e3o de cr\u00e9dito e a data de validade\ndo cart\u00e3o de cr\u00e9dito. Al\u00e9m disso, foi relatado que as informa\u00e7\u00f5es relacionadas ao pagamento de 1,2% dos\nassinantes do ChatGPT Plus tamb\u00e9m foram expostas durante a interrup\u00e7\u00e3o.\n\n[[The Hindu BusinessLine, mar\u00e7o de 2023]](https://www.thehindubusinessline.com/info-tech/openai-admits-data-breach-at-chatgpt-private-data-of-premium-users-exposed/article66659944.ece)\n\n**Precis\u00e3o inexplic\u00e1vel na previs\u00e3o de corridas**\n\nConforme o artigo de origem, pesquisadores que analisaram v\u00e1rios modelos de aprendizado de m\u00e1quina usando\nimagens m\u00e9dicas de pacientes conseguiram confirmar a capacidade dos modelos de prever a ra\u00e7a com alta\nprecis\u00e3o a partir das imagens. Eles ficaram perplexos quanto ao que exatamente est\u00e1 permitindo que os sistemas\nadivinhem corretamente de forma consistente. Os pesquisadores descobriram que at\u00e9 mesmo fatores como\ndoen\u00e7a e constitui\u00e7\u00e3o f\u00edsica n\u00e3o eram fortes preditores de ra\u00e7a, em outras palavras, os sistemas algor\u00edtmicos n\u00e3o\nparecem estar utilizando nenhum aspecto particular das imagens para fazer suas determina\u00e7\u00f5es.\n\n[[Banerjee et al., julho de 2021]](https://arxiv.org/abs/2107.10356)\n\n\n21 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n#### Exemplos de riscos: desafios\n\n\n**Grupo**\n\nControle\n\nConformidade\n\nlegal\n\n\n**Risco** **Exemplo**\n\n\nTranspar\u00eancia do Modelo:\na falta de transpar\u00eancia do\nmodelo ou documenta\u00e7\u00e3o\ninsuficiente do processo de\n\ndesenvolvimento do modelo\n\ntorna dif\u00edcil entender como\n\ne por que um modelo foi\nconstru\u00eddo, aumentando\n\nassim a possibilidade de uso\n\nindevido n\u00e3o intencional do\n\nmodelo.\n\nResponsabilidade:\no processo de\n\ndesenvolvimento de\n\nmodelos de base \u00e9\n\ncomplexo, com muitos\ndados, processos e pap\u00e9is\nenvolvidos. Quando o output\n\ndo modelo n\u00e3o funciona\n\nconforme o esperado,\npode ser dif\u00edcil determinar\n\na causa raiz e atribuir\n\nresponsabilidade.\n\nPropriedade do Conte\u00fado\n\nGerado: determinar a\n\npropriedade do conte\u00fado\ngerado por IA.\n\nPropriedade Intelectual do\n\nConte\u00fado Gerado: incerteza\n\nlegal sobre os direitos de\npropriedade intelectual\n\nrelacionados ao conte\u00fado\n\ngerado.\n\n\n**Divulga\u00e7\u00e3o de metadados de dados e modelos**\n\nO relat\u00f3rio t\u00e9cnico da OpenAI \u00e9 um exemplo da dicotomia em torno da divulga\u00e7\u00e3o de dados e metadados do\nmodelo. Embora muitos desenvolvedores de modelos reconhe\u00e7am o valor em possibilitar transpar\u00eancia para os\nconsumidores, a divulga\u00e7\u00e3o apresenta preocupa\u00e7\u00f5es reais de seguran\u00e7a e poderia aumentar a capacidade de uso\nindevido dos modelos. No relat\u00f3rio t\u00e9cnico do GPT-4, eles afirmam: \u201cdado o cen\u00e1rio competitivo e as implica\u00e7\u00f5es\nde seguran\u00e7a de modelos em larga escala como o GPT-4, este relat\u00f3rio n\u00e3o cont\u00e9m mais detalhes sobre a\narquitetura (incluindo o tamanho do modelo), hardware, computa\u00e7\u00e3o de treinamento, constru\u00e7\u00e3o do conjunto de\ndados, m\u00e9todo de treinamento ou similar.\u201d\n\n[[OpenAI, mar\u00e7o de 2023]](https://arxiv.org/pdf/2303.08774.pdf)\n\n**Determinar a responsabilidade pelo output gerado**\n\nConforme o artigo de origem, importantes revistas como a Science e a Nature proibiram o ChatGPT de ser\nlistado como autor, pois a autoria respons\u00e1vel requer responsabilidade e as ferramentas de IA n\u00e3o podem\nassumir tal responsabilidade.\n\n[[The Guardian, janeiro de 2023]](https://www.theguardian.com/science/2023/jan/26/science-journals-ban-listing-of-chatgpt-as-co-author-on-papers#:~:text=The%20publishers%20of%20thousands%20of,flawed%20and%20even%20fabricated%20research)\n\n**Determinar a Propriedade de uma Imagem Gerada por IA**\n\nDe acordo com o artigo de not\u00edcias, a arte gerada por IA se tornou controversa depois que uma obra de arte\ngerada por IA venceu a competi\u00e7\u00e3o de arte da Feira Estadual do Colorado em 2022. A pe\u00e7a foi gerada pelo\nMidjourney, uma ferramenta de imagem de IA generativa, seguindo prompts do artista. A vit\u00f3ria levantou d\u00favidas\nsobre quest\u00f5es de direitos autorais. Em outras palavras, se tudo o que o artista fez foi fornecer uma descri\u00e7\u00e3o\nda arte, mas a ferramenta de IA a gerou, quem possui os direitos da imagem gerada? Conforme o artigo mais\nrecente, o Escrit\u00f3rio de Direitos Autorais dos Estados Unidos rejeitou a prote\u00e7\u00e3o de direitos autorais para a arte\ncriada usando intelig\u00eancia artificial porque n\u00e3o foi produto de autoria humana.\n\n[[The New York Times, setembro de 2022] [Reuters, setembro de 2023]](https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html)\n\n**Papel dos sistemas de IA na patentea\u00e7\u00e3o de conte\u00fado gerado**\n\nA Suprema Corte dos Estados Unidos se recusou a ouvir uma contesta\u00e7\u00e3o \u00e0 recusa do Escrit\u00f3rio de Patentes\ne Marcas Registradas dos Estados Unidos em emitir patentes para inven\u00e7\u00f5es criadas por um sistema de IA.\nSegundo o cientista, sua IA desenvolveu prot\u00f3tipos \u00fanicos para um suporte de bebida e um farol de luz de\nemerg\u00eancia totalmente sozinha. Os ju\u00edzes rejeitaram o recurso da decis\u00e3o de um tribunal inferior de que patentes\ns\u00f3 podem ser emitidas para inventores humanos e que o sistema de IA do cientista n\u00e3o poderia ser considerado\no criador legal de duas inven\u00e7\u00f5es que ele gerou. Segundo o \u00faltimo artigo, o Intellectual Property Office do Reino\nUnido tamb\u00e9m se recusou a conceder a patente sob o argumento de que o inventor deve ser um humano ou uma\nempresa, e n\u00e3o uma m\u00e1quina.\n\n[[Reuters, abril de 2023] [Reuters, dezembro de 2023]](https://www.reuters.com/legal/us-supreme-court-rejects-computer-scientists-lawsuit-over-ai-generated-2023-04-24/)\n\n\n22 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n#### Exemplos de riscos: desafios\n\n\n**Grupo**\n\nImpacto\n\nsocial\n\n\n**Risco** **Exemplo**\n\n\nAtribui\u00e7\u00e3o da\n\nFonte: determinar\n\na proced\u00eancia do\nconte\u00fado gerado.\n\nImpacto nos\nEmpregos: a ado\u00e7\u00e3o\ngeneralizada\n\nde sistemas de\n\nIA baseados\n\nem modelos\n\nfundamentais\n\npode levar \u00e0 perda\nde empregos das\npessoas, \u00e0 medida\nque seu trabalho\n\u00e9 automatizado,\n\nse elas n\u00e3o forem\n\ncapacitadas para\n\nnovas habilidades.\n\nExplora\u00e7\u00e3o Humana:\n\nuso de trabalho\n\nfantasma (ghost\nwork) na forma\u00e7\u00e3o\n\nde modelos de\n\nIA, condi\u00e7\u00f5es\n\nde trabalho\n\ninadequadas, falta\n\nde cuidados de\n\nsa\u00fade, incluindo\n\nsa\u00fade mental e\n\ncompensa\u00e7\u00e3o\ninjusta.\n\n\n**Utilizar c\u00f3digo sem a devida atribui\u00e7\u00e3o e avisos adequados**\n\nConforme os artigos de origem, uma a\u00e7\u00e3o judicial movida contra a Microsoft, GitHub e OpenAI alegou que\no Copilot, uma ferramenta de gera\u00e7\u00e3o de c\u00f3digo de IA, viola os direitos dos desenvolvedores cujo c\u00f3digo aberto\no servi\u00e7o \u00e9 treinado. Eles afirmam que o c\u00f3digo de treinamento consumiu materiais licenciados e violou os\ntermos de servi\u00e7o e pol\u00edticas de privacidade do GitHub, bem como uma lei federal que exige que as empresas\nexibam informa\u00e7\u00f5es de direitos autorais quando fazem uso de material.\n\n[[The New York Times, novembro de 2022]](https://www.nytimes.com/2022/11/23/technology/copilot-microsoft-ai-lawsuit.html)\n\n**Substitui\u00e7\u00e3o de trabalhadores humanos**\n\nSegundo o artigo de not\u00edcias, o uso de intelig\u00eancia artificial no cinema e televis\u00e3o continua sendo debatido entre os\nest\u00fadios de Hollywood e os artistas. Existe preocupa\u00e7\u00e3o entre os atores de que os \u201cmeta-humanos\u201d, atores criados\nexclusivamente por IA, possam substitu\u00ed-los. Especialmente figurantes e dubladores est\u00e3o preocupados em perder\ntrabalho para artistas artificiais.\n\n[[Reuters, julho de 2023]](https://www.reuters.com/technology/actors-decry-existential-crisis-over-ai-generated-synthetic-actors-2023-07-21/)\n\n**Trabalhadores de baixa remunera\u00e7\u00e3o para anota\u00e7\u00e3o de dados**\n\nCom base em uma revis\u00e3o de documentos internos e entrevistas com funcion\u00e1rios pela m\u00eddia TIME, os rotuladores de\ndados empregados por uma empresa terceirizada em nome da OpenAI para identificar conte\u00fado t\u00f3xico recebiam um\nsal\u00e1rio l\u00edquido de entre cerca de US$ 1,32 e US$ 2 por hora, dependendo da senioridade e do desempenho. A TIME\nafirmou que os trabalhadores ficaram psicologicamente afetados por terem sido expostos a conte\u00fado t\u00f3xico e violento,\nincluindo detalhes gr\u00e1ficos de \u201cabuso sexual infantil, bestialidade, assassinato, suic\u00eddio, tortura, automutila\u00e7\u00e3o\n\ne incesto\u201d.\n\n[[TIME, janeiro de 2023]](https://time.com/6247678/openai-chatgpt-kenya-workers/)\n\n\n23 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n### Princ\u00edpios, pilares  e controle\n\n[Os Princ\u00edpios para Confian\u00e7a e Transpar\u00eancia da IBM e os Pilares](https://www.ibm.com/policy/trust-transparency-new/)\npara IA confi\u00e1vel s\u00e3o a base para as iniciativas de \u00e9tica em IA da IBM.\nA IBM estabeleceu um Conselho de \u00c9tica em IA com a miss\u00e3o de apoiar\n\num processo centralizado de controle, revis\u00e3o e tomada de decis\u00f5es\n\npara pol\u00edticas, pr\u00e1ticas, comunica\u00e7\u00f5es, pesquisa, produtos e servi\u00e7os\nde \u00e9tica em IA da IBM. O conselho inclui um conjunto diversificado de\nstakeholders de toda a empresa e \u00e9 apoiado por uma comunidade de\n\nfuncion\u00e1rios da IBM que atuam como pontos focais de IA e defensores\n\nda \u00e9tica em IA. Por meio do conselho, os princ\u00edpios da IBM s\u00e3o\n\ncolocados em pr\u00e1tica. Conforme novas tecnologias surgem,\ncomo modelos de base, o Conselho de \u00c9tica em IA da IBM est\u00e1\n\nativamente engajado em apoiar o alinhamento com esses Princ\u00edpios\n\ne Pilares, que evoluem para abordar novas quest\u00f5es \u00e9ticas em IA.\n\n24 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n### Prote\u00e7\u00f5es  e mitiga\u00e7\u00f5es\n\n[A IBM estabeleceu uma cultura organizacional que apoia o](https://www.ibm.com/blog/how-our-commitment-to-ethics-trust-and-transparency-is-differentiating-ibm/)\n\ndesenvolvimento e o uso respons\u00e1veis de IA. Conforme indicado no\n\n[relat\u00f3rio de \u00e9tica em a\u00e7\u00e3o na IA do IBM Institute for Business Value,](https://www.ibm.com/thought-leadership/institute-business-value/report/ai-ethics-in-action)\n\na \u00e9tica em IA j\u00e1 se tornou mais orientada pelos neg\u00f3cios do que\n\npela tecnologia, e os executivos n\u00e3o t\u00e9cnicos agora s\u00e3o os principais\n\ndefensores da \u00e9tica em IA, aumentando de 15% em 2018 para 80%\n\n3 anos depois. Al\u00e9m disso, 79% dos CEOs est\u00e3o agora preparados para\n\nagir em quest\u00f5es \u00e9ticas de IA, contra 20%. Reconhecemos que a IA\n\nrespons\u00e1vel \u00e9 uma \u00e1rea sociot\u00e9cnica que necessita de um investimento\n\nhol\u00edstico em cultura, processos e ferramentas. Nosso investimento em\n\ncultura organizacional pr\u00f3pria inclui a montagem de equipes inclusivas\n\ne multidisciplinares e o estabelecimento de processos e estruturas\n\npara avaliar riscos.\n\nA IBM est\u00e1 engajada em pesquisa de ponta e desenvolvimento de\nferramentas para ajudar os profissionais de suporte durante todo o ciclo\nde vida da IA respons\u00e1vel e confi\u00e1vel. A plataforma de IA e dados\n[empresariais watsonx, \u00e9 desenvolvida com 3 componentes: o IBM](https://www.ibm.com/br-pt/watsonx)\n\n[watsonx.ai\u2122 AI studio, o armazenamento de dados IBM watsonx.data\u2122](https://www.ibm.com/br-pt/products/watsonx-ai)\n\n[e o kit de ferramentas IBM watsonx.governance\u2122. A tecnologia de](https://www.ibm.com/br-pt/products/watsonx-governance)\n\ncontrole de IA da IBM permite que os usu\u00e1rios promovam fluxos de\n\ntrabalho de IA respons\u00e1veis, transparentes e explic\u00e1veis. Essa tecnologia\n\n[inclui o IBM Watson OpenScale, que monitora e mede os resultados dos](https://cloud.ibm.com/catalog/services/watson-openscale#about)\n\nmodelos de IA ao longo de seu ciclo de vida e auxilia as organiza\u00e7\u00f5es\n\nna supervis\u00e3o de aspectos como justi\u00e7a, explicabilidade, resili\u00eancia,\n\nalinhamento com resultados de neg\u00f3cios e conformidade. A IBM\n\ntamb\u00e9m desenvolveu v\u00e1rios m\u00e9todos para ajudar com problemas de\n\n[vi\u00e9s como FairIJ, Equi-tuning e FairReprogram. Leia mais sobre outras](https://research.ibm.com/publications/fair-infinitesimal-jackknife-mitigating-the-influence-of-biased-training-data-points-without-refitting)\n[ferramentas de IA de software livre e confi\u00e1veis.](https://research.ibm.com/topics/trustworthy-ai#tools)\n\n\nAs prote\u00e7\u00f5es e mitiga\u00e7\u00f5es adicionais incluem:\n\n**Relat\u00f3rios de transpar\u00eancia**\nUsar modelos de fichas t\u00e9cnicas padronizadas \u00e9 uma maneira de\nregistrar com precis\u00e3o detalhes sobre os dados e modelos, prop\u00f3sito\n\ne poss\u00edveis usos e riscos.\n\n[Leia mais aqui \u2192](https://newsroom.ibm.com/Whitepaper-A-Policymakers-Guide-to-Foundation-Models)\n\n**Filtragem de dados indesej\u00e1veis**\n\nUsar dados de qualidade superior e selecionados pode ajudar a\n\nmitigar determinados problemas. A IBM est\u00e1 desenvolvendo t\u00e9cnicas\nde filtragem para ajudar a reduzir as chances de produzir conte\u00fado\nindesej\u00e1vel e desalinhado por remover linguagem de \u00f3dio, linguagem\n\ntendenciosa e profanidade dos dados.\n\n[Leia mais aqui \u2192](https://research.ibm.com/blog/generative-ai-for-enterprise)\n\n**Adapta\u00e7\u00e3o de dom\u00ednio**\nTreinar um modelo de base para um dom\u00ednio ou setor espec\u00edfico pode\n\najudar a minimizar o escopo de risco para o qual os modelos podem\n\ndar origem, pois ele pode ser condicionado a gerar resultados que\n\ns\u00e3o ajustados para serem mais relevantes para esse dom\u00ednio ou setor.\n\n[Leia mais aqui \u2192](https://newsroom.ibm.com/Whitepaper-A-Policymakers-Guide-to-Foundation-Models)\n\n\n25 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n**Supervis\u00e3o humana e an\u00e1lise humana no loop**\nA supervis\u00e3o e revis\u00e3o humanas podem ajudar a identificar e corrigir\nerros e vieses no output gerado. Al\u00e9m disso, a valida\u00e7\u00e3o e o feedback\n\nhumanos sobre a qualidade das respostas do modelo ajudam a garantir\n\nque o conte\u00fado gerado seja preciso, relevante, de alta qualidade,\n\nn\u00e3o esteja divergindo e esteja alinhado.\n\n[Leia mais aqui \u2192](https://research.ibm.com/blog/generative-ai-for-enterprise)\n\n**Compromisso de consultoria**\n\nA IBM Consulting se dedica a ajudar os clientes com o uso seguro\n\ne respons\u00e1vel da IA, independentemente do stack tecnol\u00f3gico preferido.\n\nEles ajudam os clientes a cultivar uma cultura que adota e expande a\n\nIA com seguran\u00e7a, cria ferramentas de investiga\u00e7\u00e3o para ver dentro de\n\nalgoritmos de caixa preta e garante que a estrat\u00e9gia corporativa dos\n\nclientes inclua princ\u00edpios s\u00f3lidos de governan\u00e7a de dados.\n\n[Leia mais aqui \u2192](https://www.ibm.com/blog/announcement/ibm-consulting-unveils-center-of-excellence-for-generative-ai/)\n\n**IBM Enterprise Design Thinking**\n\nOs m\u00e9todos e estruturas IBM Enterprise Design Thinking, como o Team\nEssentials for AI, ajudam os clientes a definir comportamentos \u00e9ticos\n\nem todo o processo de design e desenvolvimento de IA.\n\n[Leia mais aqui \u2192](https://www.ibm.com/design/thinking/page/badges/ai)\n\n**Revis\u00e3o \u00e9tica da IA**\n\nAvalia\u00e7\u00e3o de capacidades, limita\u00e7\u00f5es e riscos em projetos de IA ajudam\n\na garantir o desenvolvimento e uso respons\u00e1vel da tecnologia.\n\n**\u00c9tica por Design**\nA \u00c9tica por Design \u00e9 um framework estruturado com o objetivo de\n\nintegrar \u00e9tica tecnol\u00f3gica no pipeline de desenvolvimento de tecnologia,\nincluindo, entre outros, sistemas de IA. A \u00c9tica por Design viabiliza IA e\n\noutras tecnologias como uma for\u00e7a para o bem, incorporando princ\u00edpios\n\nde \u00e9tica tecnol\u00f3gica em produtos, servi\u00e7os e opera\u00e7\u00f5es mais amplas.\n\n**Diversidade na equipe**\n\nA diversidade nas equipes que desenvolvem e treinam sistemas de\n\nIA, incluindo modelos de base, ajuda a garantir que uma variedade\n\nde perspectivas e experi\u00eancias sejam consideradas. Essa diversidade\n\nmelhora a precis\u00e3o e o desempenho dos sistemas de IA e ajuda a\n\nreduzir os riscos ao longo do ciclo de vida de IA, incluindo o potencial\n\npara desfechos adversos que afetam grupos que podem n\u00e3o ser bem\nrepresentados em equipes menos diversificadas.\n\n26 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n### Pol\u00edticas, regulamentos e melhores pr\u00e1ticas de IA\n\n[Um Guia dos Formuladores de Pol\u00edticas para Modelos de Base apresenta](https://newsroom.ibm.com/Whitepaper-A-Policymakers-Guide-to-Foundation-Models)\n\no que os formuladores de pol\u00edticas precisam saber sobre modelos de\n\nbase. Este blog, do Laborat\u00f3rio de Pol\u00edticas da IBM, tem como objetivo\n\najudar os formuladores de pol\u00edticas na tarefa complexa de regular\n\no uso de IA generativa, visando evitar os riscos sem limitar a inova\u00e7\u00e3o\ne as oportunidades ben\u00e9ficas. Para obter mais informa\u00e7\u00f5es sobre as\nrecomenda\u00e7\u00f5es da IBM aos formuladores de pol\u00edticas, leia o depoimento\nda Diretora de Privacidade e Confian\u00e7a da IBM, Christina Montgomery,\ndiante da Subcomiss\u00e3o Judici\u00e1ria de Privacidade, Tecnologia e Lei do\n\n[Senado dos EUA aqui.](https://www.judiciary.senate.gov/imo/media/doc/2023-05-16 - Testimony - Montgomery.pdf)\n\nA IBM est\u00e1 causando um impacto na forma\u00e7\u00e3o de pol\u00edticas regulat\u00f3rias,\n\nmelhores pr\u00e1ticas e ferramentas do setor, controle de tecnologias\n\nemergentes e pesquisa sociot\u00e9cnica, liderando e contribuindo\n\npara iniciativas com organiza\u00e7\u00f5es como:\n\n\u2013 O F\u00f3rum Econ\u00f4mico Mundial\n\n\u2013 Parceria em IA\n\n\u2013 Centro de controle de IA da Associa\u00e7\u00e3o Internacional de Profissionais\n\nde Privacidade (IAPP)\n\n\u2013 Iniciativa global de IEEE sobre \u00e9tica de sistemas aut\u00f4nomos e\n\ninteligentes\n\u2013 Participa\u00e7\u00e3o de Christina Montgomery do National Artificial\n\nIntelligence Advisory Committee (NAIAC)\n\n\u2013 O Pacto Digital Global das Na\u00e7\u00f5es Unidas\n\n\u2013 A Parceria Global em Intelig\u00eancia Artificial (GPAI)\n\n\u2013 A Organiza\u00e7\u00e3o para Coopera\u00e7\u00e3o e Desenvolvimento Econ\u00f4mico\n\n(OECD)\n\n\u2013 A Data & Trust Alliance\n\n\nA IBM tem parcerias acad\u00eamicas s\u00f3lidas, como o MIT-IBM Watson\n\nAI Lab, onde uma comunidade de cientistas do MIT e da IBM Research\n\nconduzem pesquisas sobre IA e trabalham com organiza\u00e7\u00f5es globais\n\npara unir algoritmos ao seu impacto nos neg\u00f3cios e na sociedade.\n\nO Notre Dame-IBM Tech Ethics Lab foi formado para abordar as diversas\n\nquest\u00f5es \u00e9ticas implicadas pelo desenvolvimento e uso de tecnologias\n\navan\u00e7adas, incluindo IA, aprendizado de m\u00e1quina (ML) e computa\u00e7\u00e3o\nqu\u00e2ntica. A pesquisa de Intelig\u00eancia Artificial Centrada no Homem (HAI)\nda Universidade de Stanford promove pesquisas, educa\u00e7\u00e3o, pol\u00edticas\n\ne pr\u00e1ticas de IA.\n\n\n27 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n#### Continue acompanhando este espa\u00e7o para obter mais informa\u00e7\u00f5es sobre os \u00faltimos avan\u00e7os em modelos de base e como a IBM est\u00e1 trabalhando para o desenvolvimento respons\u00e1vel e uso desta e de outras tecnologias.\n\n28 Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es | fevereiro de 2024\n\n\n-----\n\n\u00a9 Copyright IBM Corporation 2023, 2024\n\nIBM Brasil Ltda\n\nRua Tut\u00f3ia, 1157\n\nCEP 04007-900\n\nS\u00e3o Paulo, SP\nIBM Corporation\nNew Orchard Road\n\nArmonk, NY 10504\n\nProduzido nos\n\nEstados Unidos da Am\u00e9rica\n\nFevereiro de 2024\n\nIBM, o logotipo da IBM, Enterprise Design Thinking, IBM Consulting, IBM Research,\nIBM Watson, watsonx, watsonx.ai, watsonx.data e watsonx.governance s\u00e3o marcas\ncomerciais ou marcas registradas da International Business Machines Corporation,\nnos Estados Unidos e/ou em outros pa\u00edses. Outros nomes de produtos e servi\u00e7os\npodem ser marcas comerciais da IBM ou de outras empresas. Uma lista atual de\n[marcas comerciais da IBM est\u00e1 dispon\u00edvel em ibm.com/br-pt/trademark.](https://www.ibm.com/br-pt/thought-leadership/trademark/)\n\nEste documento \u00e9 atual na data de sua publica\u00e7\u00e3o inicial, podendo ser alterado\npela IBM a qualquer momento. Nem todas as ofertas est\u00e3o dispon\u00edveis em todos os\npa\u00edses nos quais a IBM opera.\n\nAS INFORMA\u00c7\u00d5ES CONTIDAS NESTE DOCUMENTO S\u00c3O FORNECIDAS NO ESTADO\nEM QUE SEM ENCONTRAM, SEM QUALQUER GARANTIA, EXPRESSA OU IMPL\u00cdCITA,\nINCLUSIVE SEM QUALQUER GARANTIA DE COMERCIALIZA\u00c7\u00c3O, ADEQUA\u00c7\u00c3O A\nDETERMINADO FIM E QUALQUER GARANTIA OU CONDI\u00c7\u00c3O DE N\u00c3O INFRA\u00c7\u00c3O.\nOs produtos IBM t\u00eam a garantia prevista nos termos e condi\u00e7\u00f5es dos contratos sob\nos quais s\u00e3o fornecidos.\n\nDeclara\u00e7\u00e3o de boas pr\u00e1ticas de seguran\u00e7a: nenhum sistema ou produto de TI deve\nser considerado completamente seguro, e nenhuma medida exclusiva de produto,\nservi\u00e7o ou seguran\u00e7a pode ser completamente eficaz na preven\u00e7\u00e3o de uso ou\nacesso inadequado. A IBM n\u00e3o garante que nenhum de seus sistemas, produtos\nou servi\u00e7os estejam imunes nem que tornar\u00e3o sua empresa imune a condutas\nmaliciosas ou ilegais por parte de terceiros.\n\nO cliente \u00e9 respons\u00e1vel por garantir o cumprimento de todas as leis e regulamentos\naplic\u00e1veis. A IBM n\u00e3o fornece conselho jur\u00eddico tampouco representa ou garante\nque seus servi\u00e7os ou produtos garantir\u00e3o que o cliente esteja em conformidade\ncom qualquer lei ou regulamenta\u00e7\u00e3o. Todas as declara\u00e7\u00f5es relativas ao\ndirecionamento e \u00e0s inten\u00e7\u00f5es da IBM no futuro est\u00e3o sujeitas a altera\u00e7\u00f5es ou\nretirada sem aviso pr\u00e9vio e representam apenas metas e objetivos.\n\n\n-----\n\n", "id": "c0f799a6-2051-49a7-874b-465c3ab10787", "media": [{"id": "c0f799a6-2051-49a7-874b-465c3ab10787", "type": 3, "url": "https://www.ibm.com/downloads/cas/A0W84W1V", "alt": null, "path": "000_00000.bin.gz", "offset": 342329828, "media_bytes": null, "metadata": {"Content-Type": "application/pdf", "X-Dispatcher": "prod-disp1", "X-Vhost": "publish", "Server-Timing": "intid;desc=3f9f4e99118b169d", "Content-Disposition": "filename=\"foundation-model-ethics_whitepaper_final.pdf\"", "Last-Modified": "Fri, 12 Apr 2024 15:24:43 GMT", "cf-cache-status": "DYNAMIC", "Server": "cloudflare", "CF-RAY": "92a2ec684e985e82-EWR", "Content-Encoding": "gzip", "Cache-Control": "max-age=31536000", "Expires": "Thu, 02 Apr 2026 20:02:22 GMT", "Date": "Wed, 02 Apr 2025 20:02:22 GMT", "Transfer-Encoding": "chunked", "Connection": "keep-alive, Transfer-Encoding", "Vary": "Accept-Encoding", "Set-Cookie": "__cflb=0H28vcyNox31dfEfHry9N9JcS8hfTNjPQrfEqMTBr9y; SameSite=Lax; path=/; expires=Wed, 02-Apr-25 21:02:22 GMT; HttpOnly, _abck=A7C860C888BA24FCBDC56D8413168AA8~-1~YAAQWGvcF6O8Eu6VAQAAhf8Y+A02nQpjkeWrmvDaPQclRVMhBhHcLtdJwHPEShDeVRiXSrbJBVZpGrWVjDeWcnmjhfQJY5/tVTej3St/xhn9ECnuoBCrYGwx5VRg4fmeVxw36eDSnhyWzYbH3IAMWWU+oEj5OuIEs+CLhKda5oO87OBpXZdff5GeZjhOYnYhfvYIsYarCuJ5S/ax+bhwdaCDrKl2760nrpQcc02TfC6/nPRLzondHB+IT06quiGL5aTRIyOYJTKkUIfliOUDLgEPrud6PR2mqVdTlEv3ZpAwYSBxPZw4aEKtW5lkIL+mVOtcy0hyj5w0xsEMHXxvQjb5X8PU8PS95LoJGlewZMoelJhqJqjtkKEUqyBJsFFwZj/+jXodZwkBLOxalbi2OIOi910QPSk=~-1~-1~-1; Domain=.ibm.com; Path=/; Expires=Thu, 02 Apr 2026 20:02:22 GMT; Max-Age=31536000; Secure, bm_sz=0C53AF3CB27EC5F7B698EEF6A57B90C3~YAAQWGvcF6S8Eu6VAQAAhf8Y+BtGLhL7NVvsrqFZ4ixMM0irytptoxn8YH34wUWisS6vzzLby3bmmTL0pz83e9aiKssq6NeZHOjgCsOvpy6OJxbaEGn6LBv6HlC1hvt225pw4BQMck9h+sVtD+lwp6qhKKC+Xvu8szD6aU0zF4DjbtHJgFEObd2RAfFhaJEPj5/gp2MgIawlnKLU2vXKkhfmltR7cHmIhmjr2nkJZXYLLUZ7/NTFMFmv+iKIhtpizCmqNXUQa/uIagsWelB/zWQlIB+FCUrZY0lIcUxS7ve/8Lu0QjBaoDhZ57jrGaTkz//Lsv2zXDvSAFH3+TLDNGdgi+6hudOMrA==~4276789~3487028; Domain=.ibm.com; Path=/; Expires=Thu, 03 Apr 2025 00:02:21 GMT; Max-Age=14399", "X-Robots-Tag": "noindex, nofollow, none, noarchive, nosnippet, noodp, notranslate, noimageindex", "x-content-type-options": "nosniff", "X-XSS-Protection": "1; mode=block", "Content-Security-Policy": "upgrade-insecure-requests", "Strict-Transport-Security": "max-age=31536000", "pdf_metadata": {"num_pages": 29, "page_offsets": [99, 778, 1092, 2911, 5628, 8654, 10070, 11549, 15558, 19400, 22138, 25951, 29521, 32986, 35790, 39309, 43462, 45704, 49007, 52623, 55713, 59969, 62639, 63677, 67377, 69784, 72133, 72431, 74620], "format": "PDF 1.7", "title": "Modelos de base: oportunidades, riscos e mitiga\u00e7\u00f5es", "author": "IBM", "subject": "Explore o ponto de vista do Conselho de \u00c9tica em IA da IBM sobre a cria\u00e7\u00e3o e uso respons\u00e1vel de modelos de base.", "keywords": "Modelos de base; IA generativa; thought leadership; \u00e9tica em IA; IA; \u00e9tica; IA confi\u00e1vel; IA respons\u00e1vel; governan\u00e7a de IA; IA generativa; LLMs.", "creator": "Adobe InDesign 18.3 (Macintosh)", "producer": "Adobe PDF Library 17.0", "creationDate": "D:20240409141010+02'00'", "modDate": "D:20240412114335-03'00'", "trapped": "", "encryption": null}}}], "metadata": {"index_file": "part-00227-4f628544-3cdf-4526-86aa-bdfa0b33cdc9.c000.gz.parquet", "url": "https://www.ibm.com/downloads/cas/A0W84W1V", "fetch_time": "2024-08-16T04:35:50+00:00", "fetch_status": 200, "content_mime_type": "application/pdf", "content_mime_detected": "application/pdf", "content_languages": null, "content_truncated": "length", "warc_filename": "crawl-data/CC-MAIN-2024-33/segments/1722641333615.45/warc/CC-MAIN-20240816030812-20240816060812-00101.warc.gz", "warc_record_offset": 624718862, "file_path": "s3://commoncrawl/cc-index/table/cc-main/warc/crawl=CC-MAIN-2024-33/subset=warc/part-00227-4f628544-3cdf-4526-86aa-bdfa0b33cdc9.c000.gz.parquet", "extractor": "pymupdf_llm"}}}