Book Title: Computational Criminology: AI Applications in Forensic Science and Criminal Justice
Editors: Dr. Xavier Louis, Dr. Surbhi Girdhar, Ms. Aswathi Chandran Nair, Mr. Ravi Kumar, and Ms. Nandini Katare
Chapter: 5
DOI: https://doi.org/10.59646/704/5
Author: Shivendra Pratap Singh
Abstract
Few applications of computational methods in criminal justice have generated as much enthusiasm, controversy, and disappointment in as compressed a period as predictive policing. In the decade between 2010 and 2020, vendors and police agencies promised to legislators, to journalists, and to publics that statistical and machine-learning models could anticipate crime in space, in time, and in particular individuals with sufficient accuracy to support a fundamental reorganization of patrol practice. By the close of that decade, several of the most prominent flagship programmes had been quietly discontinued, their efficacy disputed, their fairness contested, and their constitutional foundations subject to active litigation.[1]The trajectory of predictive policing in this period is not merely an episode in the recent history of police technology; it is a case study in the gap between methodological promise and institutional reality, and a cautionary template for the rest of computational criminology. This chapter examines that trajectory analytically rather than polemically.
It treats predictive policing neither as a self-evidently scientific practice rendered controversial only by misunderstanding nor as a self-evidently illegitimate practice unworthy of serious technical scrutiny. The reality, as the chapter argues, is more difficult: predictive policing comprises a heterogeneous family of analytical approaches with markedly different evidentiary support, operational characteristics, and ethical implications, and a responsible assessment requires the careful disaggregation of these approaches rather than wholesale endorsement or rejection.[2]
[1]Walter Perry and others, Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations (RAND Corporation, 2013) 1.
[2]Andrew Guthrie Ferguson, The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement (New York University Press, 2017) 64.