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Accountable algorithms
Reference Type:
Journal Article
Kroll, Joshua, Joanna Huey, Solon Barocas, Edward Felten, Joel Reidenberg, David Robinson, and Harlan Yu. 2017. “Accountable algorithms.” University of Pennsylvania Law Review 165 (3): 633. https://scholarship.law.upenn.edu/penn_law_review/vol165/iss3/3
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 legal fairness.
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