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113 Geo. L.J. 53 (2024-2025)
Less Discriminatory Algorithms

handle is hein.journals/glj113 and id is 56 raw text is: Less Discriminatory Algorithms EMILY B1ACK*t, JOHN LOGAN KOEPKE**t, PAULINE T. KIM***, SOLON BAROCAS**** & M1NGwE1 Hsu***** In discussions about algorithms and discrimination, it is often assumed that machine learning techniques will identify a unique solution to any given prediction problem, such that any attempt to develop less discrimi- natory models will inevitably entail a tradeoff with accuracy. Contrary to this conventional wisdom, however, computer science has established that multiple models with equivalent performance exist for a given pre- diction problem. This phenomenon, termed model multiplicity, suggests that when an algorithmic system displays a disparate impact, there almost always exists a less discriminatory algorithm (LDA) that performs equally well. But without dedicated exploration, developers are unlikely to discover potential LDAs. These observations have profound ramifica- tions for the legal and policy response to discriminatory algorithms. Because the overarching purpose of our civil rights laws is to remove ar- bitrary barriers to full participation by marginalized groups in the nation's economic life, the law should place a duty to search for LDAs on entities that develop and deploy predictive models in domains covered by civil rights laws, like housing, employment, and credit. The law should recognize this duty in at least two specific ways. First, under disparate impact doctrine, a defendant's burden of justifying a model with discrim- inatory effects should include showing that it made a reasonable search for LDAs before implementing the model. Second, new regulatory frame- works for the governance of algorithms should include a requirement that entities search for and implement LDAs as part of the model building process. * Assistant Professor, Department of Computer Science and Engineering and the Center for Data Science, New York University. © 2024, Emily Black, John Logan Koepke, Pauline T. Kim, Solon Barocas & Mingwei Hsu. ** Project Director, Upturn. *** Daniel Noyes Kirby Professor of Law, Washington University School of Law, St. Louis, Missouri. **** Principal Researcher, Microsoft Research; Adjunct Associate Professor, Information Science, Cornell University. ***** Senior Quantitative Analyst, Upturn. t Equal contribution. tt The authors would like to thank the following individuals for their helpful feedback: Olga Akselrod, Elizabeth Edenberg, Talia Gillis, Stephen Hayes, Daniel Jellins, Cynthia Khoo, Michael McGovern, Paul Ohm, Catherine Powell, Manish Raghavan, Matthew Scherer, Andrew Selbst, Ridhi Shetty, Eric Sublett, Dan Svirsky, Suresh Venkatasubramanian, and the staff of Upturn. The authors are also grateful to the participants at the 2023 Privacy Law Scholars Conference, the participants at the Law & Technology Workshop, the Washington University School of Law faculty workshop for their comments, to Julia Monti and Kelly Miller for excellent research assistance, and to the Editors at The Georgetown Law Journal for their careful and thoughtful editorial work to bring this Article to print. 53

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