predictive-policing

Chapter 6, "Less Crime, More Punishment," explores the rise and implications of predictive policing technologies and data-driven crime prevention methods. It critically examines how algorithms and surveillance tools are used to anticipate and prevent crime, while raising ethical concerns about privacy, automation of virtue, and the social consequences of making crime 'impossible.' The chapter also discusses the balance between technological control and human judgment, emphasizing the need for ongoing moral debate and the risks of over-reliance on automated systems.

Chapter 5 examines the use of predictive policing software like PredPol in economically struggling cities such as Reading, Pennsylvania, highlighting how these models rely on historical crime data to allocate police resources. While these models aim to reduce serious crimes by focusing on geographic hotspots, the inclusion of nuisance crimes disproportionately targets impoverished and minority neighborhoods, creating a feedback loop that perpetuates racial and economic disparities. The chapter critiques the zero-tolerance policing approach and contrasts the under-policing of financial crimes with the over-policing of minor offenses in poor communities.

Chapter 5 of 'Weapons of Math Destruction' examines the use of predictive policing technologies such as PredPol and facial recognition software in various U.S. cities, highlighting their roots in historical policing strategies like zero-tolerance and stop-and-frisk. The chapter critiques these algorithms for perpetuating racial biases and inefficiencies, noting the disproportionate targeting of minority communities and the problematic incentives of private prisons. It also references studies and legal challenges that question the effectiveness and fairness of these data-driven policing methods.