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2020 U. Ill. J.L. Tech. & Pol'y 1 (2020)
Contextual Fairness: A Legal and Policy Analysis of Algorithmic Fairness

handle is hein.journals/jltp2020 and id is 7 raw text is: CONTEXTUAL FAIRNESS: A LEGAL AND POLICY ANALYSIS OF ALGORITHMIC FAIRNESS Doaa Abu-Elyounest Abstract To date, all stakeholders are working intensively on policy design for artificial intelligence. All initiatives center around the requirement that AI algorithms should be fair. But what exactly does it mean? And how can algorithmic fairness be translated to legal and policy terms? These are the main questions that this paper aims to explore. Each discipline approaches those questions differently. While computer scientists may favor one notion offairness over others across the board, this paper argues in favor of a case-by-case analysis and application of the relevant fairness notion. The paper discusses the legal limitations of the computer science (CS) notions of fairness and suggests a typology of matching each CS notions to its corresponding legal mechanism. The paper concludes that fairness is contextual. The fact that each notion, or group of notions, correspond with a different legal mechanism, makes them suitable for a certain policy domain more than others. Thus, throughout the paper, examples for possible applicability of the CS notions to some policy domains will be introduced. In addition, the paper will highlight for both developers and policymakers the practical steps that need to be taken in order to better address algorithmic fairness. In some instances, notions offairness that seem, on their face, unproductive from a technical perspective, could in fact be quite helpful from a legal perspective. In other instances, desirable notions in the eye of computer scientists could be challenging to implement in the legal regime, due to the need to determine complex moral and legal questions. Thus, as the article t Author - Harvard Law School; Berkman Klein Center for Internet and Society at Harvard University (email: dabuelyounes@sjd.law.harvard.edu). I would like to express my gratitude to my advisor, Professor Yochai Benkler, and to Professor Cynthia Dwork, Professor Martha Minow and Professor Niva Elkin-Koren for an extremely helpful guidance throughout the writing process. I would also like to thank Christina Ilvento, Yonadav Shavit, Boriana Gjura, Meena Jagadeesan, Joshua Simons and Zhun Deng for helpful comments on the technical CS aspects of the paper, as well as the participants of Ethical Tech working group at Berkman Klein Center for their useful advice. Earlier drafts of this work were presented in the Cambridge International Law Conference and the Al and Law Conference in Montreal and I am grateful for helpful comments provided by the participants. This work was supported by a grant from the Center for Cyber Law and Policy at the University of Haifa, Israel. I

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