Police facial recognition applications and violent crime control in U.S. cities

Abstract

This study presents novel insights into the effects of police facial recognition applications on violent crime and arrest dynamics across 268 U.S. cities from 1997 to 2020. We conducted generalized difference-in-differences regressions with multiway fixed effects to exploit this technology's staggered implementation. Our findings indicate that police facial recognition applications facilitate reductions in the rates of felony violence and homicide without contributing to over-policing or racial disparities in arrest for violent offenses. Greater reductions were observed for cities that adopted these technologies earlier in the study period, suggesting that their public safety benefits appreciate over time. The results of parallel trend and robustness tests also support these conclusions. While further research is necessary to assess the implementation and effects of facial recognition systems in various contexts, presented evidence suggests that urban police agencies that responsibly deploy these innovations to support crime control efforts can keep their residents safer and reduce the lives lost to violence.