000			01700nam a22001337a 4500
999			_c530166 _d530166
008			250529b ||||| |||| 00| 0 eng d
100			_aSchiff, Kaylyn Jackson _953904
245			_aInstitutional factors driving citizen perceptions of AI in government: Evidence from a survey experiment on policing
260			_aPublic Administration Review
300			_a85(2), Mar, 2025: p.451-467
520			_aLaw 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.- Reproduced https://onlinelibrary.wiley.com/doi/10.1111/puar.13754
773			_aPublic Administration Review
942			_cAR