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Combatting Police Discrimination in the Age of Big Data

20 New Crim. L. Rev. 181 (2017)

Stanford Public Law Working Paper No. 2787101

52 Pages Posted: 1 Jun 2016 Last revised: 2 Apr 2017

Sharad Goel

Stanford University

Maya Perelman

Central District of California

Ravi Shroff

New York University (NYU)

David Alan Sklansky

Stanford University

Date Written: May 31, 2016

Abstract

The exponential growth of available information about routine police activities offers new opportunities to improve the fairness and effectiveness of police practices. We illustrate the point by showing how a particular kind of calculation made possible by modern, large-scale datasets — determining the likelihood that stopping and frisking a particular pedestrian will result in the discovery of contraband or other evidence of criminal activity — could be used to reduce the racially disparate impact of pedestrian searches and to increase their effectiveness. For tools of this kind to achieve their full potential in improving policing, though, the legal system will need to adapt. One important change would be to understand police tactics such as investigatory stops of pedestrians or motorists as programs, not as isolated occurrences. Beyond that, the judiciary will need to grow more comfortable with statistical proof of discriminatory policing, and the police will need to be more receptive to the assistance that algorithms can provide in reducing bias.

Keywords: Police, Discrimination, Stop-And-Frisk, Statistical Proof, Big Data

Suggested Citation

Goel, Sharad and Perelman, Maya and Shroff, Ravi and Sklansky, David Alan, Combatting Police Discrimination in the Age of Big Data (May 31, 2016). 20 New Crim. L. Rev. 181 (2017); Stanford Public Law Working Paper No. 2787101. Available at SSRN: https://ssrn.com/abstract=2787101

Sharad Goel

Stanford University ( email )

475 Via Ortega
Stanford, CA 94305
United States

HOME PAGE: http://5harad.com

Maya Perelman

Central District of California ( email )

3470 Twelfth Street
Riverside, CA 92501
United States

Ravi Shroff

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

David Alan Sklansky (Contact Author)

Stanford University ( email )

Stanford, CA 94305
United States

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