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165 U. Pa. L. Rev. 633 (2016-2017)
Accountable Algorithms

handle is hein.journals/pnlr165 and id is 648 raw text is: ARTICLE ACCOUNTABLE ALGORITHMS JOSHUA A. KROLL, JOANNA HUEY, SOLON BAROCAS, EDWARD W. FELTEN, JOEL R. REIDENBERG, DAVID G. ROBINSON & HARLAN YUt Many important decisions historically made by people are now made by computers. Algorithms count votes, approve loan and credit card applications, target citizens or neighborhoods for police scrutiny, select taxpayers for IRS audit, grant or deny immigration visas, and more. The accountability mechanisms and legal standards that govern such decision processes have not kept pace with technology. The tools currently available to policymakers, legislators, and courts were developed to oversee human decisionmakers and often fail when applied to computers instead. For example, how do you judge the intent of a piece of software? Because automated decision systems can return potentially incorrect, unjustified, or unfair results, additional approaches are needed to make such systems accountable and governable. This Article reveals a new technological toolkit to verify that automated decisions comply with key standards of legalfairness. We challenge the dominant position in the legal literature that transparency will solve these problems. Disclosure of source code is often neither necessary (because of alternative techniques from computer science) nor sufficient (because of the issues analyzing code) to demonstrate the fairness of a process. Furthermore, transparency t Respectively, Affiliate, Center for Information Technology Policy, Princeton; Associate Director, Center for Information Technology Policy, Princeton; Post Doctoral Research Associate, Princeton; Robert E. Kahn Professor of Computer Science and Public Affairs, Princeton; Stanley D. and Nikki Waxberg Chair in Law, Fordham Law School; Principal, Upturn, and Visiting Fellow, Information Society Project, Yale Law School; Principal, Upturn, and Fellow, Stanford Center for Internet and Society. For helpful comments, the authors are very grateful to participants at the Berkeley Privacy Law Scholars Conference and at the NYU School of Law conference on 'Accountability and Algorithms. Research on this Article was supported in part by NSF Award DGE-11489oo and a Fordham Faculty Fellowship. (633)

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