The Socio-Economic Impacts of Predictive Policing on Minority Communities and Potential Solutions

Abstract

Black defendants are 77% more likely to be labeled high-risk for violent recidivism than white defendants, even when accounting for prior offenses, age, gender, and other factors. This significant disparity highlights the urgency of addressing racial bias in predictive policing algorithms in the criminal justice system. While technologies of predictive policing were designed to enhance efficiency in law enforcement, they have quietly embedded systemic racial biases that are devastating for minority communities. This paper examines how predictive policing models contribute to cycles of over-policing and socio-economic inequality by relying on historically biased data. My analysis presents the devastating consequences of wrongful arrests, economic hardship, and psychological trauma, calling for urgent reforms. It outlines a way forward in the quest for fairer and more ethical use of predictive technologies. This paper aims to ensure policing technologies serve all communities equitably by improving data quality, integrating restorative justice practices, and establishing robust oversight policies.