Combatting Police Discrimination in The Age of Big Data

Solon Barocas

Sharad Goel, Maya Perelman, Ravi Shroff, David Alan Sklansky

The growth of available information about routine police activities offers new opportunities to improve the fairness and effectiveness of police practices. We illustrate this 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.

Goel, Sharad, Maya Perelman, Ravi Shroff, and David Alan Sklansky. "Combatting Police Discrimination in the Age of Big Data." New Criminal Law Review (2016), Forthcoming.

Slides

https://speakerdeck.com/fatml/combatting-police-discrimination-in-the-age-of-big-data