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87 Fordham L. Rev. 1085 (2018-2019)
The Intuitive Appeal of Explainable Machines

handle is hein.journals/flr87 and id is 1118 raw text is: THE INTUITIVE APPEAL OF EXPLAINABLE MACHINES Andrew D. Selbst* & Solon Barocas** Algorithmic decision-making has become synonymous with inexplicable decision-making, but what makes algorithms so difficult to explain? This Article examines what sets machine learning apart from other ways of developing rules for decision-making and the problem these properties pose for explanation. We show that machine learning models can be both inscrutable and nonintuitive and that these are related, but distinct, properties. Calls for explanation have treated these problems as one and the same, but disentangling the two reveals that they demand very different responses. Dealing with inscrutability requires providing a sensible description of the rules; addressing nonintuitiveness requires providing a satisfying explanation for why the rules are what they are. Existing laws like the Fair Credit Reporting Act (FCRA), the Equal Credit Opportunity Act (ECOA), * Postdoctoral Scholar, Data & Society Research Institute; Visiting Fellow, Yale Information Society Project. Selbst is grateful for the support of the National Science Foundation under grant IIS 1633400. ** Assistant Professor, Cornell University, Department of Information Science. For helpful comments and insights on earlier drafts, the authors would like to thank Jack Balkin, Rabia Belt, danah boyd, Kiel Brennan-Marquez, Albert Chang, Danielle Citron, Julie Cohen, Lilian Edwards, Sorelle Freidler, Giles Hooker, Margaret Hu, Karen Levy, Margot Kaminski, R6ndn Kennedy, Been Kim, Jon Kleinberg, Brian Kreiswirth, Chandler May, Brent Mittelstadt, Deidre Mulligan, David Lehr, Paul Ohm, Helen Nissenbaum, Frank Pasquale, Nicholson Price, Manish Raghavan, Aaron Rieke, David Robinson, Ira Rubinstein, Matthew Salganik, Katherine Strandburg, Sandra Wachter, Hanna Wallach, Cody Marie Wild, Natalie Williams, Jennifer Wortman Vaughan, Michael Veale, Suresh Venkatasubramanian, and participants at the following conferences and workshops: NYUInnovation Colloquium, NYU School of Law, February 2017; We Robot, Yale Law School, March 2017; Big Data Ethics Colloquium, The Wharton School, Philadelphia, PA, April 2017; NYU Algorithms and Explanations Conference, NYU School of Law, April 2017; TILTing Perspectives, Tilburg University, the Netherlands, May 2017; Privacy Law Scholars' Conference, Berkeley, CA, June 2017; Summer Faculty Workshop, Georgetown University Law Center, June 2017; Explainable and Accountable Algorithms Workshop, Alan Turing Institute, UK, January 2018. Special thanks to Chandler May for graphics that sadly did not make it into the final draft, and to the editors of the Fordham Law Review for their excellent and professional work getting this Article ready for publication. This Article is available for reuse under the Creative Commons Attribution- NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-sa/4.0/. The required attribution notice under the license must include the Article's full citation information, e.g., Andrew D. Selbst & Solon Barocas, The Intuitive Appeal of Explainable Machines, 87 FORDHAM L. REv. 1085 (2018). 1085

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