Algorithmic Policing

• In Event: Bias and Discrimination in the Criminal Legal System and Beyond

Abstract

Predictive policing tools — algorithms that predict crime risk at hyperlocal levels and high frequency — are increasingly used in the hopes of improving efficiency. This paper examines the impacts of algorithmic policing and how it affects racial profiling. Using a novel dataset on predictive policing from a major urban US jurisdiction and a natural experiment research design, I isolate quasi-experimental variation in algorithm-induced police presence. I find that algorithmic policing decreases serious violent and property crimes but exacerbates racial disparities in arrests in traffic incidents and serious violent crimes. The evidence suggests a threefold increase in arrests of Black motorists when the neighborhood is targeted in comparison to when it is not. These results reveal that algorithmic policing can prevent crime at the cost of increasing racial disparities in arrests, underscoring the racial equity implications of algorithmic targeting.

Author

• Ranae Jabri, University of Sydney