Who Trusts the Algorithm? Racial Identity, Political Orientation, and Public Support for AI in Policing

• In Event: Crime, Law and Deviance Section Roundtables
  In Refereed Roundtable (60 minute): 2: Technology and the Criminal Justice System

Abstract

The rapid integration of artificial intelligence (AI) into law enforcement has generated significant scholarly and public debate, yet public attitudes toward police AI remain insufficiently understood. This study examines the independent effects of racial and ethnic minority status, political orientation, and AI familiarity on public support for AI in policing. Drawing on survey data collected from a large online sample (N = 952) via Prolific, this study employs means tests and OLS regression to analyze attitudes toward thirteen distinct police AI applications. Results indicate that political orientation is the strongest and most consistent predictor of support, with conservative identifying respondents expressing significantly higher support across all thirteen technologies. Racial and ethnic minority status was associated with lower support for predictive policing and facial recognition but did not achieve statistical significance in multivariate analysis once political orientation and other covariates were controlled. Frequency of AI use emerged as the second strongest predictor, consistent with a familiarity based diffusion framework. These findings suggest that public attitudes toward police AI are primarily structured by ideological predispositions toward law enforcement authority rather than demographic characteristics alone. Implications for democratic governance, community engagement, and the equitable deployment of AI in policing are discussed.

Authors

• Gang Lee, Kennesaw State University

• Sinyong Choi