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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.

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