Abstract Law enforcement agencies are increasingly adopting artificial intelligence (AI)‐powered tools. While prior work emphasizes the technological features driving public opinion, we investigate how public trust and support for AI in government vary with the institutional context. We administer a pre‐registered survey experiment to 4200 respondents about AI use cases in policing to measure responsiveness to three key institutional factors: bureaucratic proximity (i.e., local sheriff versus national Federal Bureau of Investigation), algorithmic targets (i.e., public targets via predictive policing versus detecting officer misconduct through automated case review), and agency capacity (i.e., necessary resources and expertise). We find that the public clearly prefers local over national law enforcement use of AI, while reactions to different algorithmic targets are more limited and politicized. However, we find no responsiveness to agency capacity or lack thereof. The findings suggest the need for greater scholarly, practitioner, and public attention to organizational, not only technical, prerequisites for successful government implementation of AI.
A survey of 4,200 people found that Americans are more supportive of local police departments using artificial intelligence tools than federal agencies like the FBI, but they show little concern about whether agencies have the necessary resources and expertise to use AI properly. The research reveals that public opinion about AI in policing depends heavily on which agency is using the technology rather than what the technology actually does or how well-prepared the agency is to implement it. This matters because it suggests police departments need to focus on building community trust and demonstrating local accountability when introducing AI tools, rather than just proving the technology works.
(AI-generated summary, v1, January 2026)
Citations: 31 (as of January 2026)