FAIRNESS MEETS MACHINE LEARNING: SEARCHING FOR A BETTER BALANCE

Ekaterina Semenova (), Ekaterina Perevoshchikova (), Alexey Ivanov () and Mikhail Erofeev ()
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Ekaterina Semenova: National Research University Higher School of Economics
Ekaterina Perevoshchikova: National Research University Higher School of Economics
Alexey Ivanov: National Research University Higher School of Economics
Mikhail Erofeev: Lomonosov Moscow State University

HSE Working papers from National Research University Higher School of Economics

Abstract: Machine learning (ML) affects nearly every aspect of our lives, including the weightiest ones such as criminal justice. As it becomes more widespread, however, it raises the question of how we can integrate fairness into ML algorithms to ensure that all citizens receive equal treatment and to avoid imperiling society’s democratic values. In this paper we study various formal definitions of fairness that can be embedded into ML algorithms and show that the root cause of most debates about AI fairness is society’s lack of a consistent understanding of fairness generally. We conclude that AI regulations stipulating an abstract fairness principle are ineffective societally. Capitalizing on extensive related work in computer science and the humanities, we present an approach that can help ML developers choose a formal definition of fairness suitable for a particular country and application domain. Abstract rules from the human world fail in the ML world and ML developers will never be free from criticism if the status quo remains. We argue that the law should shift from an abstract definition of fairness to a formal legal definition. Legislators and society as a whole should tackle the challenge of defining fairness, but since no definition perfectly matches the human sense of fairness, legislators must publicly acknowledge the drawbacks of the chosen definition and assert that the benefits outweigh them. Doing so creates transparent standards of fairness to ensure that technology serves the values and best interests of society

Keywords: Artificial Intelligence; Bias; Fairness; Machine Learning; Regulation; Values; Antidiscrimination Law (search for similar items in EconPapers)
JEL-codes: K19 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2019
New Economics Papers: this item is included in nep-big, nep-cmp, nep-hpe, nep-law and nep-pay
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Published in WP BRP Series: Law / LAW, December 2019, pages 1-18

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Persistent link: https://EconPapers.repec.org/RePEc:hig:wpaper:93/law/2019

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