Algorithmic Policing

Abstract

Predictive policing algorithms are increasingly used by law enforcement agencies in the United States. These algorithms use past crime data to generate predictive policing boxes, specifically the highest crime risk areas where law enforcement is instructed to patrol every shift. I collect a novel dataset on predictive policing box locations, crime incidents, and arrests from a major urban jurisdiction where predictive policing is used. Using institutional features of the predictive policing policy, I isolate quasi-experimental variation to examine the causal impacts of algorithm-induced police presence. I find that algorithm-induced police presence decreases serious property and violent crime. At the same time, I also find disproportionate racial impacts on arrests for serious violent crimes as well as arrests in traffic incidents i.e. lower-level offenses where police have discretion. These results highlight that using predictive policing to target neighborhoods can generate a tradeoff between crime prevention and equity.

Keywords: algorithms, predictive policing, law enforcement, crime, neighborhoods, big data, inequality, race

JEL Classification: K40, J15, K42, H0, C53