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CRIME PREVENTION

CRIME PREVENTION-POLICING-CRIME REDUCTION-POLITICS

Law Enforcement Use of Person-Based Predictive Policing Approaches: Proceedings of a Workshop—in Brief

By National Academies of Sciences, Engineering, and Medicine; Division of Behavioral and Social Sciences and Education; Erin Hammers Forstag, Rapporteur

On June 24 and 25, 2024, the National Academies of Sciences, Engineering, and Medicine held a two-day public workshop exploring law enforcement’s use of person-based and place-based predictive policing strategies. Predictive policing strategies are approaches that use data to attempt to predict either individuals who are likely to commit crime or places where crime is likely to be committed, to enable crime prevention. The workshop was held in response to Executive Order 14074,1 which discusses enhancing public trust and safety through accountable policing and criminal justice practices, and Executive Order 14110,2 which addresses the use of artificial intelligence (AI) in law enforcement. David Weisburd (George Mason University and Chair of the workshop planning committee) began by noting that these executive orders reflected strong public concerns surrounding the idea of predictive policing, as well as critiques of specific implementations—in particular for these strategies’ disparate impact on communities of color. While planning the workshop, Weisburd said that the planning committee confronted several challenging issues. First, there is a lack of precise and clear definitions of what exactly constitutes predictive policing. Second, the term “predictive policing” is often avoided, even when approaches appear to meet conventional definitions. Predictive technologies include “automated,” “dynamic,” or “data-driven,” approaches. However, predictive policing is generally seen as involving predictive algorithms that identify individuals and locations that are more likely to be associated with crime in the future. Whatever the definition, law enforcement agencies routinely use tools that collect and analyze data to anticipate crime and facilitate police response. Weisburd highlighted that the method and extent to which police should rely on algorithmic approaches remain as real-world challenges for law enforcement officials.

This workshop, said Weisburd, comes at a time when original applications of predictive policing have come and gone, while algorithmic and big data technologies advance and continue to be applied in law enforcement contexts. “We may be on the precipice of a new era of predictive policing,” he said, “with the time and wisdom to consider what that could and should look like.”

Washington, DC: National Academies Press, 2024. 13p.