Referências

Referências Bibliográficas – IA Responsável

  1. Áreas de Interesse:

1- IA, Comunicação e Arte

​​EPSTEIN, Ziv et al. Who gets credit for AI-generated art?. Iscience, v. 23, n. 9, 2020.

CETINIC, Eva; SHE, James. Understanding and creating art with AI: Review and outlook. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), v. 18, n. 2, p. 1-22, 2022.

MAZZONE, Marian; ELGAMMAL, Ahmed. Art, creativity, and the potential of artificial intelligence. In: Arts. MDPI, 2019. p. 26.

2- IA e Economia

DUAN, Yanqing; EDWARDS, John S.; DWIVEDI, Yogesh K. Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, v. 48, p. 63-71, 2019.

AGRAWAL, Ajay; GANS, Joshua; GOLDFARB, Avi. Prediction Machines, Updated and Expanded: The Simple Economics of Artificial Intelligence. Harvard Business Press, 2022.

AGRAWAL, Ajay; GANS, Joshua S.; GOLDFARB, Avi. Artificial intelligence: the ambiguous labor market impact of automating prediction. Journal of Economic Perspectives, v. 33, n. 2, p. 31-50, 2019.

3- IA e Gênero

DI VAIO, Assunta; HASSAN, Rohail; PALLADINO, Rosa. Blockchain technology and gender equality: A systematic literature review. International Journal of Information Management, v. 68, p. 102517, 2023.

FRENNERT, Susanne. Gender blindness: On health and welfare technology, AI and gender equality in community care. Nursing Inquiry, v. 28, n. 4, p. e12419, 2021.

LEE, Michelle S.; GUO, Lisa N.; NAMBUDIRI, Vinod E. Towards gender equity in artificial intelligence and machine learning applications in dermatology. Journal of the American Medical Informatics Association, v. 29, n. 2, p. 400-403, 2022.

LÜTZ, Fabian. Gender equality and artificial intelligence in Europe. Addressing direct and indirect impacts of algorithms on gender-based discrimination. In: ERA forum. Berlin/Heidelberg: Springer Berlin Heidelberg, 2022. p. 33-52.

MARTIN, Nicole. The Role of AI, Technology and Education in Gender Equality. ITNOW, v. 65, n. 4, p. 26-27, 2023.

RÖNNBLOM, Malin; CARLSSON, Vanja; ÖJEHAG‐PETTERSSON, Andreas. Gender equality in Swedish AI policies. What’s the problem represented to be?. Review of Policy Research, 2023.

WONGSUPPAKAN, Woratep. AI and Gender Equality on Twitter. Feminist AI, 2023.

4- IA e Governo

CAMPION, Averill et al. Overcoming the challenges of collaboratively adopting artificial intelligence in the public sector. Social Science Computer Review, v. 40, n. 2, p. 462-477, 2022.

LOI, Michele; SPIELKAMP, Matthias. Towards accountability in the use of artificial intelligence for public administrations. In: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. 2021. p. 757-766.

GARSON, G. David. The promise of digital government. In: Digital government: Principles and best practices. Igi Global, 2004. p. 2-15.

HILLER, Janine S.; BÉLANGER, France. Privacy strategies for electronic government. E-government, v. 200, n. 2001, p. 162-198, 2001.

KE, Weiling; WEI, Kwok Kee. Successful e-government in Singapore. Communications of the ACM, v. 47, n. 6, p. 95-99, 2004.

PNG, Marie-Therese. At the tensions of south and north: Critical roles of global south stakeholders in AI governance. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 2022. p. 1434-1445.

UTHMANN, Tabea. Transition towards digital modernity in the Global South: The contribution of AI to sustainable development in Sub-Saharan African countries. 2021. Trabalho de Conclusão de Curso. University of Twente.

5- IA e Justiça

BRUNDAGE, Miles et al. The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228, 2018.

CROOTOF, Rebecca. “CYBORG JUSTICE” AND THE RISK OF TECHNOLOGICAL–LEGAL LOCK-IN. Columbia Law Review, v. 119, n. 7, p. 233-251, 2019.

CROOTOF, Rebecca. The killer robots are here: legal and policy implications. Cardozo L. Rev., v. 36, p. 1837, 2014.

SCHERER, Maxi. Artificial Intelligence and Legal Decision-Making: The Wide Open?. Journal of international arbitration, v. 36, n. 5, 2019.

SOURDIN, Tania. Judge v Robot?: Artificial intelligence and judicial decision-making. University of New South Wales Law Journal, The, v. 41, n. 4, p. 1114-1133, 2018.

6- IA e Meio Ambiente

WU, Carole-Jean et al. Sustainable ai: Environmental implications, challenges and opportunities. Proceedings of Machine Learning and Systems, v. 4, p. 795-813, 2022

VAN WYNSBERGHE, Aimee. Sustainable AI: AI for sustainability and the sustainability of AI. AI and Ethics, v. 1, n. 3, p. 213-218, 2021.

DHAR, Payal. The carbon impact of artificial intelligence. Nat. Mach. Intell., v. 2, n. 8, p. 423-425, 2020.

7- IA e Racismo

ZOU, James; SCHIEBINGER, Londa. AI can be sexist and racist—it’s time to make it fair. 2018.

GUPTA, Manjul; PARRA, Carlos M.; DENNEHY, Denis. Questioning racial and gender bias in AI-based recommendations: Do espoused national cultural values matter?. Information Systems Frontiers, p. 1-17, 2021.

NTOUTSI, Eirini et al. Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, v. 10, n. 3, p. e1356, 2020.

DAUGHERTY, Paul R.; WILSON, H. James; CHOWDHURY, Rumman. Using artificial intelligence to promote diversity. MIT Sloan Management Review, 2018

8- IA e Saúde

MORLEY, Jessica et al. The ethics of AI in health care: a mapping review. Social Science & Medicine, v. 260, p. 113172, 2020.

KELLY, Christopher J. et al. Key challenges for delivering clinical impact with artificial intelligence. BMC medicine, v. 17, p. 1-9, 2019.

MAGRABI, Farah et al. Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearbook of medical informatics, v. 28, n. 01, p. 128-134, 2019.

ADADI, Amina; BERRADA, Mohammed. Explainable AI for healthcare: from black box to interpretable models. In: Embedded Systems and Artificial Intelligence: Proceedings of ESAI 2019, Fez, Morocco. Springer Singapore, 2020. p. 327-337.

CHEN, Irene Y.; SZOLOVITS, Peter; GHASSEMI, Marzyeh. Can AI help reduce disparities in general medical and mental health care?. AMA journal of ethics, v. 21, n. 2, p. 167-179, 2019.

9- IA e Trabalho

DERANTY, Jean-Philippe; CORBIN, Thomas. Artificial intelligence and work: a critical review of recent research from the social sciences. AI & SOCIETY, p. 1-17, 2022.

PARRY, Emma; BATTISTA, Valentina. The impact of emerging technologies on work: a review of the evidence and implications for the human resource function. Emerald Open Research, v. 1, n. 5, p. 5, 2019.

MAKRIDAKIS, Spyros. The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, v. 90, p. 46-60, 2017.

  1. Princípios:

i- Accountability

BUSUIOC, Madalina. Accountable artificial intelligence: Holding algorithms to account. Public Administration Review, v. 81, n. 5, p. 825-836, 2021.

CROOTOF, Rebecca. War torts: Accountability for autonomous weapons. U. Pa. L. Rev., v. 164, p. 1347, 2015.

NOVELLI, Claudio; TADDEO, Mariarosaria; FLORIDI, Luciano. Accountability in artificial intelligence: what it is and how it works. AI & SOCIETY, p. 1-12, 2023

WIERINGA, Maranke. What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. In: Proceedings of the 2020 conference on fairness, accountability, and transparency. 2020. p. 1-18.

BINNS, Reuben. Algorithmic accountability and public reason. Philosophy & technology, v. 31, n. 4, p. 543-556, 2018.

STAHL, Bernd Carsten et al. A systematic review of artificial intelligence impact assessments. Artificial Intelligence Review, p. 1-33, 2023.

ii- Explicabilidade

ARRIETA, Alejandro Barredo et al. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities, and challenges toward responsible AI. Information fusion, v. 58, p. 82-115, 2020.

DOSHI-VELEZ, Finale; KIM, Been. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608, 2017.

LIPTON, Zachary C. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, v. 16, n. 3, p. 31-57, 2018.

MILLER, Tim. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence, v. 267, p. 1-38, 2019.

MOLNAR, Christoph. Interpretable machine learning: A Guide for Making Black Box Models Explainable. 2022.

OKOLO, Chinasa T.; DELL, Nicola; VASHISTHA, Aditya. Making AI explainable in the Global South: A systematic review. In: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS). 2022. p. 439-452.

VAINIO-PEKKA, Heidi et al. The Role of Explainable AI in the Research Field of AI Ethics. ACM Transactions on Interactive Intelligent Systems, 2023.

iii- Fairness

CORBETT-DAVIES, Sam; GOEL, Sharad. The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv preprint arXiv:1808.00023, 2018.

CORBETT-DAVIES, Sam et al. The measure and mismeasure of fairness. arXiv preprint arXiv:1808.00023, 2023.

JOHN-MATHEWS, Jean-Marie; CARDON, Dominique; BALAGUÉ, Christine. From reality to world. A critical perspective on AI fairness. Journal of Business Ethics, v. 178, n. 4, p. 945-959, 2022.

NAKAO, Yuri et al. Towards responsible AI: A design space exploration of human-centered artificial intelligence user interfaces to investigate fairness. International Journal of Human–Computer Interaction, v. 39, n. 9, p. 1762-1788, 2023.

iv- Safety

AIZENBERG, Evgeni; VAN DEN HOVEN, Jeroen. Designing for human rights in AI. Big Data & Society, v. 7, n. 2, p. 2053951720949566, 2020.

MODEI, Dario et al. Concrete problems in AI safety. arXiv preprint arXiv:1606.06565, 2016.

LESLIE, David. Understanding artificial intelligence ethics and safety. arXiv preprint arXiv:1906.05684, 2019.

v- Transparência

KOIVISTO, Ida. The anatomy of transparency: The concept and its multifarious implications. 2016.

TURILLI, Matteo; FLORIDI, Luciano. The ethics of information transparency. Ethics and Information Technology, v. 11, p. 105-112, 2009.

CREEL, Kathleen A. Transparency in complex computational systems. Philosophy of Science, v. 87, n. 4, p. 568-589, 2020.

WANG, Hao. Why Should We Care About the Manipulative Power of Algorithmic Transparency?. Philosophy & Technology, v. 36, n. 1, p. 9, 2023.

OFEM, Paulinus; ISONG, Bassey; LUGAYIZI, Francis. On the concept of transparency: A systematic literature review. IEEE Access, v. 10, p. 89887-89914, 2022.

LAPOSTOL PIDERIT, José Pablo; GARRIDO IGLESIAS, Romina; HERMOSILLA CORNEJO, María Paz. Algorithmic Transparency from the South: Examining the state of algorithmic transparency in Chile’s public administration algorithms. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 2023. p. 227-235.

BOMMASANI, Rishi et al. The Foundation Model Transparency Index. arXiv preprint arXiv:2310.12941, 2023.

ESLAMI, Motahhare et al. User attitudes towards algorithmic opacity and transparency in online reviewing platforms. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. p. 1-14.

vi- Trustworthy AI

SMUHA, Nathalie A. The EU approach to ethics guidelines for trustworthy artificial intelligence. Computer Law Review International, v. 20, n. 4, p. 97-106, 2019.

FLORIDI, Luciano. Establishing the rules for building trustworthy AI. Ethics, Governance, and Policies in Artificial Intelligence, p. 41-45, 2021.

vii- Referências que citam mais de um princípio:

KAMINSKI, Margot E. Understanding transparency in algorithmic accountability. Forthcoming in Cambridge Handbook of the Law of Algorithms, ed. Woodrow Barfield, Cambridge University Press (2020)., U of Colorado Law Legal Studies Research Paper, n. 20-34, 2020.

VON ESCHENBACH, Warren J. Transparency and the black box problem: Why we do not trust AI. Philosophy & Technology, v. 34, n. 4, p. 1607-1622, 2021.

KEMPER, Jakko; KOLKMAN, Daan. Transparent to whom? No algorithmic accountability without a critical audience. Information, Communication & Society, v. 22, n. 14, p. 2081-2096, 2019.

RADER, Emilee; COTTER, Kelley; CHO, Janghee. Explanations as mechanisms for supporting algorithmic transparency. In: Proceedings of the 2018 CHI conference on human factors in computing systems. 2018. p. 1-13.

  1. Referências gerais que mesclam áreas e princípios:

ARUN, Chinmayi. AI and the global south: Designing for other worlds. 2019.

BAEZA-YATES, Ricardo. LECTURE HELD AT THE ACADEMIA EUROPAEA BUILDING BRIDGES CONFERENCE 2022: An Introduction to Responsible AI. European Review, v. 31, n. 4, p. 406-421, 2023.

BENJAMINS, Richard; BARBADO, Alberto; SIERRA, Daniel. Responsible AI by design in practice. arXiv preprint arXiv:1909.12838, 2019.

BURRELL, Jenna. How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big data & society, v. 3, n. 1, p. 2053951715622512, 2016.

HAGERTY, Alexa; RUBINOV, Igor. Global AI ethics: a review of the social impacts and ethical implications of artificial intelligence. arXiv preprint arXiv:1907.07892, 2019.

MEMARIAN, Bahar; DOLECK, Tenzin. Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI), and higher education: A systematic review. Computers and Education: Artificial Intelligence, p. 100152, 2023.

SHAHRIARI, Kyarash; SHAHRIARI, Mana. IEEE standard review—Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems. In: 2017 IEEE Canada International Humanitarian Technology Conference (IHTC). IEEE, 2017. p. 197-201.

SOLYST, Jaemarie et al. “I Would Like to Design”: Black Girls Analyzing and Ideating Fair and Accountable AI. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 2023. p. 1-14.

WHITTAKER, Meredith et al. AI now report 2018. New York: AI Now Institute at New York University, 2018.