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123 W. Va. L. Rev. 735 (2020-2021)
Bias Preservation in Machine Learning: The Legality of Fairness Metrics under EU Non-Discrimination Law

handle is hein.journals/wvb123 and id is 765 raw text is: BIAS PRESERVATION IN MACHINE LEARNING: THE LEGALITY OF FAIRNESS METRICS UNDER EU NON-DISCRIMINATION LAW Sandra Wachter, * Brent Mittelstadt, ** and Chris Russell*** Abstract Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train machine learning. Algorithms trained on biased data will, without intervention, produce biased outcomes and increase the inequality experienced by historically disadvantaged groups. Recognizing this problem, much work has emerged in recent years to test for bias in machine learning and AI systems using various fairness and bias metrics. Often these fairness metrics address technical bias, but not the underlying cause of inequality: social bias. In this Article we make three contributions. First, we assess the compatibility offairness metrics used in machine learning against the aims and purpose of EU non-discrimination law. We show that the fundamental aim of the law is not only to prevent ongoing discrimination, but also to change society, policies, and practices to level the playing field and achieve substantive rather than merely formal equality. Based on this, we then propose a novel classification scheme for fairness metrics in machine learning based on how they handle pre-existing bias and thus align with the aims of non-discrimination law. Specifically, we distinguish between bias preserving and bias transforming fairness metrics. Our classification system is intended to bridge the gap between non-discrimination law and decisions around how to measure fairness in machine learning and AI in practice. Finally, we show that the legal need for justification in cases of indirect discrimination * Oxford Internet Institute, University of Oxford, 1 St. Giles, Oxford, OXI 3JS, UK. Email: sandra.wachter@oii.ox.ac.uk ** Oxford Internet Institute, University of Oxford, 1 St. Giles, Oxford, OXI 3JS, UK. *** Amazon Web Services, Inc. A great thank you is owed to the Harvard Law School, its faculty and students, the participants of the Harvard Law Faculty's Workshop, and the members of the Berkman Klein Center for Internet & Society for the inspiring discussions during Wachter's research visit in Spring 2020. The authors are also indebted to Dr Silvia Milano, Dr Johann Laux, and Prof Philipp Hacker for their detailed and immensely valuable feedback that greatly improved the quality of the paper. This paper would not exist without Jade Thompson, thank you for opening eyes, hearts, and minds, for caring and making others care. The paper also benefitted significantly from the insightful comments and diligent work of the editorial team at West Virginia Law Review. This work of the Governance of Emerging Technologies research programme at the Oxford Internet Institute has been supported by the British Academy Postdoctoral Fellowship grant nr PF2\180114 and grant nr PF\170151 from the Omidyar Group, and Miami Foundation. 735

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