Stop and Risk: Policing, Data, and the Digital Age of Discrimination

Abstract

Predictive policing is the newest innovation in the field of law enforcement. Predictive policing programs use algorithms to analyze existing crime data in an attempt to make predictions about future crimes: What crimes are likely to be committed, where crimes are likely to be committed, and a list of potential victims and offenders. Proponents of predictive policing champion the practice as an effective, proactive form of law enforcement that is free from bias due to its data-driven nature. However, as a matter of justice policy, predictive policing is just as discriminatory as traditional police practices, such as stop and frisk: Both are relatively ineffective; both have the potential to disproportionately target minorities; both are challenging forms of surveillance that create several important ethical and legal issues; and both are presented as objective, impartial, and equitable. This article has three primary goals: Highlight the potential and problematic similarities between stop and frisk and predictive policing; present the problems associated with predictive policing, including its questionable effectiveness, biased foundation, and faulty legal and ethical footing; and discuss the ways in which discriminatory criminal justice programs, such as stop and frisk and predictive policing, are presented to the public as objective, non-discriminatory policies.