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104 Calif. L. Rev. 671 (2016)
Big Data's Disparate Impact

handle is hein.journals/calr104 and id is 695 raw text is: 









             Big Data's Disparate Impact



                  Solon Barocas* & Andrew D. Selbst* *



          Advocates of algorithmic techniques like data mining argue that
     these techniques eliminate human biases from the decision-making
     process. But an algorithm is only as good as the data it works with.
     Data is frequently imperfect in ways that allow these algorithms to
     inherit the prejudices of prior decision makers. In other cases, data
     may simply reflect the widespread biases that persist in society at
     large. In still others, data mining can discover surprisingly useful
     regularities that are really just preexisting patterns of exclusion and
     inequality. Unthinking reliance on data mining can deny historically
     disadvantaged and vulnerable groups full participation in society.
     Worse still, because the resulting discrimination is almost always an
     unintentional emergent property of the algorithm's use rather than a
     conscious choice by its programmers, it can be unusually hard to
     identify the source of the problem or to explain it to a court.

           This Essay examines these concerns through the lens of
     American antidiscrimination law-more particularly, through Title



          DOI: http://dx.doi.org/10.15779/Z38BG31
          California Law Review, Inc. (CLR) is a California nonprofit corporation. CLR and the
authors are solely responsible for the content of their publications.
      * Postdoctoral Research Associate, Center for Information Technology Policy, Princeton
University; Ph.D. 2014, New York University, Department of Media, Culture, and Communication.
This research was supported in part by the Center for Information Technology Policy at Princeton
University.
     **   Scholar in Residence, Electronic Privacy Information Center; Visiting Researcher,
Georgetown University Law Center; Visiting Fellow, Yale Information Society Project; J.D. 2011,
University of Michigan Law School. The authors would like to thank Jane Bambauer, Alvaro Bedoya,
Majory Blumenthal, Danielle Citron, James Grimmelmann, Moritz Hardt, Don Herzog, Janine Hiller,
Chris Hooffiagle, Joanna Huey, Patrick Ishizuka, Michael Kirkpatrick, Aaron Konopasky, Joshua
Kroll, Mark MacCarthy, Arvind Narayanan, Helen Norton, Paul Ohm, Scott Pepper, Joel Reidenberg,
David Robinson, Kathy Strandburg, David Vladeck, members of the Privacy Research Group at New
York University, and the participants of the 2014 Privacy Law Scholars Conference for their helpful
comments. Special thanks also to Helen Nissenbaum and the Information Law Institute at New York
University for giving us an interdisciplinary space to share ideas, allowing this paper to come about.
Copyright © 2016 by Solon Barocas and Andrew Selbst. This Essay is available for reuse under the
Creative     Commons       Attribution-ShareAlike   4.0      International   License,
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., Solon Barocas & Andrew D. Selbst, Big Data's
Disparate Impact, 104 CALIF. L. REv. 671 (2016).

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