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105 Iowa L. Rev. 1257 (2019-2020)
Proxy Discrimination in the Age of Artificial Intelligence and Big Data

handle is hein.journals/ilr105 and id is 1283 raw text is: Proxy Discrimination in the Age of Artificial Intelligence and Big Data Anya E.R. Prince &Daniel Schwarcz* ABSTRACT: Big data and Artificial Intelligence (A) are revolutionizing the ways in which firms, governments, and employers classify individuals. Surprisingly, however, one of the most important threats to anti- discrimination regimes posed by this revolution is largely unexplored or misunderstood in the extant literature. This is the risk that modern algorithms will result in 'proxy discrimination. Proxy discrimination is a particularly pernicious subset of disparate impact. Like allforms of disparate impact, it involves a facially neutral practice that disproportionately harms members of a protected class. But a practice producing a disparate impact only amounts to proxy discrimination when the usefulness to the discriminator of thefacially neutral practice derives, at least in part, from the very fact that it produces a disparate impact. Historically, this occurred when afirm intentionally sought to discriminate against members of a protected class by relying on a proxy for class membership, such as zip code. However, proxy discrimination need not be intentional when membership in a protected class is predictive of a discriminator's facially neutral goal, making discrimination rational. In these cases, firms may unwittingly proxy discriminate, knowing only that a facially neutral practice produces desirable outcomes. This Article argues that AI and big data are game changers when it comes to this risk of unintentional, but rational, proxy discrimination. As armed with big data are inherently structured to engage in proxy discrimination whenever they are deprived of information about membership in a legally suspect class whose predictive power cannot be measured more directly by non-suspect data available to the AL Simply denying AIs access to the most intuitive proxies for such predictive but suspect characteristics does little to thwart this process; instead it simply causes As to locate less intuitive proxies. For these reasons, * Anya E.R. Prince (anya-prince@uiowa.edu) is an Associate Professor, University of Iowa College of Law. Daniel Schwarcz (Schwarcz@umn.edu) is the Fredrikson & Byron Professor of Law, University of Minnesota Law School. For comments and suggestions on preliminary drafts, we thank Ken Abraham, Ronen Avraham, Jessica Clarke, 1. Glenn Cohen, James Grimmelmnan, Jill Hasday, Claire Hill, Dave Jones, Sonia Katyal, Pauline Kim, Kyle Logue, Peter Molk, Chris Odinet, Nicholson Price, Jessica Roberts, Andrew Selbst, Elizabeth Sepper, Rory Van Loo and participants of the Consumer Law Conference at Berkeley Law School. 1257

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