Institutional factors driving citizen perceptions of AI ingovernment: evidence from a survey experiment on policing - Schiff, Kaylyn Jackson (Public administration review, 2025)
Law enforcement agencies are increasingly adopting artificial intelligence (AI)-powered tools. While prior work emphasizes the technological features drivingpublic opinion, we investigate how public trust and support for AI in governmentvary with the institutional context. We administer a pre-registered survey experi-ment to 4200 respondents about AI use cases in policing to measure responsive-ness to three key institutional factors: bureaucratic proximity (i.e., local sheriffversus national Federal Bureau of Investigation), algorithmic targets (i.e., public tar-gets via predictive policing versus detecting officer misconduct through auto-mated case review), and agency capacity (i.e., necessary resources and expertise).We find that the public clearly prefers local over national law enforcement use ofAI, while reactions to different algorithmic targets are more limited and politicized.However, we find no responsiveness to agency capacity or lack thereof. The find-ings suggest the need for greater scholarly, practitioner, and public attention toorganizational, not only technical, prerequisites for successful government imple-mentation of AI.