Risk, race, and predictive policing: A critical race theory analysis of the strategic subject list

Abstract

Predictive policing is a tool used increasingly by police departments that may exacerbate entrenched racial/ethnic disparities in the Prison Industrial Complex (PIC). Using a Critical Race Theory framework, we analyzed arrest data from a predictive policing program, the Strategic Subject List (SSL), and questioned how the SSL risk score (i.e., calculated risk for gun violence perpetration or victimization) predicts the arrested individual's race/ethnicity while accounting for local spatial conditions, including poverty and racial composition. Using multinomial logistic regression with community area fixed effects, results indicate that the risk score predicts the race/ethnicity of the arrested person while accounting for spatial context. As such, despite claims of scientific objectivity, we provide empirical evidence that the algorithmically‐derived risk variable is racially biased. We discuss our study in the context of how the SSL reinforces a pseudoscientific justification of the PIC and call for the abolition of these tools broadly.