This Article examines the role military automated surveillance and intelligence systems and techniques have supported a self-reinforcing racial bias when used by civilian police departments to enhance predictive policing programs. I will focus on two facets of this problem. First, my research will take an inside-out perspective, studying the role played by advanced military technologies and methods within civilian police departments, and how they have enabled a new focus on deterrence and crime prevention by creating a system of structural surveillance where decision support relies increasingly upon algorithms and automated data analysis tools, and which automates de facto penalization and containment based on race. Second, I will explore these systems—and their effects—from an outside-in perspective, paying particular attention to racial, societal, economic, and geographic factors that play into the public perception of these policing regimes. I will conclude by proposing potential solutions to this problem, which incorporate tests for racial bias to create an alternative system that follows a true community policing model.
Constitutional law, law & society, law & technology, race & ethnicity, bias, law enforcement, militarization, intelligence gathering, structural surveillance, algorithmic & community policing, observation, orientation, decision, action, broken windows, Compstat, stop & frisk, search & seizure
Michigan Journal of Race & Law
Vagle, Jeffrey L., "Tightening the OODA Loop: Police Militarization, Race, and Algorithmic Surveillance" (2016). Faculty Scholarship at Penn Law. 1630.
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