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

handle is hein.journals/pnlr165 and id is 648 raw text is: 


                    ACCOUNTABLE ALGORITHMS

                                &  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-t489oo  and a Fordham Faculty Fellowship.


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