Detalhes

Nome da Disciplina: TÓPICOS ESPECIAIS III: ENGENHARIA DE INTELIGÊNCIA ARTIFICIAL APLICADA À INVESTIGAÇÃO CIENTÍFICA

Carga Horária: 30

Créditos: 2

Obrigatória: Não

Introdução aos fundamentos da inteligência artificial como ferramenta de apoio à investigação científica. Exploração de ferramentas comerciais e de código aberto para análise de dados, revisão de literatura, redação científica e modelagem. Ética e responsabilidade no uso de IA na pesquisa científica/acadêmica. Boas práticas para implementação de soluções de IA em contextos de investigação. Avaliação crítica de resultados gerados por sistemas de IA.

Bibliografia Básica: RUSSELL, S.; NORVIG, P. Artificial Intelligence: A Modern Approach. 4th ed. Pearson, 2020. FLORIDI, L. The Ethics of Information. Oxford University Press, 2019. GOODFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep Learning. MIT Press, 2016. JOBIN, A.; IENCA, M.; VAYENA, E. The global landscape of AI ethics guidelines. Nature Machine Intelligence, v. 1, p. 389-399, 2019. MARCUS, G.; DAVIS, E. Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon Books, 2019. Bibliografia Complementar: ALPAYDIN, E. Machine Learning: The New AI. MIT Press, 2016. BAROCAS, S.; HARDT, M.; NARAYANAN, A. Fairness and Machine Learning: Limitations and Opportunities. MIT Press, 2023. BENJAMIN, R. Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press, 2019. CATHY O'NEIL. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing, 2016. DIGNUM, V. Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer, 2019. EUBANKS, V. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press, 2018. GILLESPIE, T. Algorithmically recognizable: Santorum's Google problem, and Google's Santorum problem. Information, Communication & Society, v. 20, n. 1, p. 6380, 2017. HAENLEIN, M.; KAPLAN, A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, v. 61, n. 4, p. 5-14, 2019. JAMES, G.; WITTEN, D.; HASTIE, T.; TIBSHIRANI, R. An Introduction to Statistical Learning with Applications in R. 2nd ed. Springer, 2021. MITCHELL, T. Machine Learning. McGraw-Hill, 1997. MURPHY, K. Machine Learning: A Probabilistic Perspective. MIT Press, 2012. NOBLE, S. U. Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press, 2018. SALGANIK, M. Bit by Bit: Social Research in the Digital Age. Princeton University Press, 2017. ZUBOFF, S. The Age of Surveillance Capitalism. PublicAffairs, 2019. Artigos Científicos Essenciais: BEAM, A. L.; KOHANE, I. S. Big Data and Machine Learning in Health Care. JAMA, v. 319, n. 13, p. 1317-1318, 2018. CHEN, J.; SONG, L.; WAINWRIGHT, M.; JORDAN, M. Learning to Explain: An Information-Theoretic Perspective on Model Interpretation. Proceedings of the 35th International Conference on Machine Learning, 2018. DOSHI-VELEZ, F.; KIM, B. Towards A Rigorous Science of Interpretable Machine Learning. arXiv preprint arXiv:1702.08608, 2017. HOLSTEIN, K. et al. Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019. LIPTON, Z. C. The Mythos of Model Interpretability. Communications of the ACM, v. 61, n. 10, p. 36-43, 2018. RAJKOMAR, A.; DEAN, J.; KOHANE, I. Machine Learning in Medicine. New England Journal of Medicine, v. 380, n. 14, p. 1347-1358, 2019. RIBEIRO, M. T.; SINGH, S.; GUESTRIN, C. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. Diretrizes e Documentos Institucionais: Commission. Ethics Guidelines for Trustworthy AI. High-Level Expert Group on Artificial Intelligence, 2019. IEEE. Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems. IEEE Standards Association, 2019. Nature. Editorial: Tools such as ChatGPT threaten transparent science; here are our ground rules for their use. Nature, v. 613, p. 612, 2023. Partnership on AI. About ML: A Human-Centered Approach to Machine Learning. Partnership on AI, 2019. Science. Editorial: ChatGPT and other generative AI tools cannot be credited as authors. Science, v. 379, n. 6630, p. 313, 2023. Recursos Online e Plataformas: AI Ethics Guidelines Global Search. Disponível em: https://aiethicslab.com/bigpicture/ Elements of AI. University of Helsinki. Disponível em: https://www.elementsofai.com/ Ethics in AI Course. MIT OpenCourseWare. Disponível em: https://ocw.mit.edu/ Google AI Education. Disponível em: https://ai.google/education/ Microsoft AI for Good Research Lab. Disponível em: https://www.microsoft.com/enus/research/group/ai-for-good-research-lab/ OpenAI Safety Research. Disponível em: https://openai.com/safety The Ethics of AI. Stanford HAI. Disponível em: https://hai.stanford.edu/