Institutional Factors Driving Citizen Perceptions of AI in Government: Evidence from a Survey Experiment on Policing
Practitioners in public administration increasingly employ AI tools. This study delves into contextual factors influencing public reactions to AI in policing, exploring bureaucratic proximity, algorithmic targets, and agency capacity. Results show public favor for AI in local law enforcement, but preferences vary based on political affiliation, race, and the purpose of AI application.
Executive Summary
Law enforcement agencies are increasingly adopting AI-powered tools, including predictive policing and automated misconduct detection systems. This study explores how public trust and support for AI in government are influenced by institutional factors like bureaucratic proximity (local vs. federal law enforcement), algorithmic targets (public-focused predictive policing vs. internal misconduct detection), and agency capacity. Using a pre-registered survey experiment with 4,200 respondents, findings reveal strong public preference for local over federal use of AI tools. Partisan and racial differences emerge in support for different AI applications, but agency capacity appears less influential. These results underscore the importance of organizational context in public attitudes towards AI in governance.