20 New Crim. L. Rev. 181 (2017)
52 Pages Posted: 1 Jun 2016 Last revised: 2 Apr 2017
Date Written: May 31, 2016
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: 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