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What's Fair about Individual Fairness?

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (2021)

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  1. Algorithmic Fairness Criteria as Evidence.Will Fleisher - forthcoming - Ergo: An Open Access Journal of Philosophy.
    Statistical fairness criteria are widely used for diagnosing and ameliorating algorithmic bias. However, these fairness criteria are controversial as their use raises several difficult questions. I argue that the major problems for statistical algorithmic fairness criteria stem from an incorrect understanding of their nature. These criteria are primarily used for two purposes: first, evaluating AI systems for bias, and second constraining machine learning optimization problems in order to ameliorate such bias. The first purpose typically involves treating each criterion as a (...)
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  • Dirty data labeled dirt cheap: epistemic injustice in machine learning systems.Gordon Hull - 2023 - Ethics and Information Technology 25 (3):1-14.
    Artificial intelligence (AI) and machine learning (ML) systems increasingly purport to deliver knowledge about people and the world. Unfortunately, they also seem to frequently present results that repeat or magnify biased treatment of racial and other vulnerable minorities. This paper proposes that at least some of the problems with AI’s treatment of minorities can be captured by the concept of epistemic injustice. To substantiate this claim, I argue that (1) pretrial detention and physiognomic AI systems commit testimonial injustice because their (...)
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  • Trust and Explainable AI: Promises and Limitations.Sara Blanco - 2022 - Ethicomp Conference Proceedings.
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  • 14th Scandinavian Conference on Artificial Intelligence SCAI 2024.Florian Westphal, Einav Peretz-Andersson, Maria Riveiro, Kerstin Bach & Fredrik Heintz (eds.) - 2024 - Linköping, Sweden: Linköping Electronic Conference Proceedings 208.
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