Institutional factors driving citizen perceptions of AI in government: Evidence from a survey experiment on policing

• 成果类型: Article
• 署名作者: Schiff, Kaylyn Jackson; Schiff, Daniel S.; Adams, Ian T.; McCrain, Joshua; Mourtgos, Scott M.
• 署名单位: Purdue University System; Purdue University; University of South Carolina System; University of South Carolina Columbia; Utah System of Higher Education; University of Utah
• 刊物名称: PUBLIC ADMINISTRATION REVIEW
• ISSN/ISSBN: 0033-3352
• DOI: 10.1111/puar.13754
• 发表日期: 2025
• 页码: 451-467
• 关键词: plate recognition technology; artificial-intelligence; public-opinion; support; performance; governance; DISCRETION; service; trust

摘要

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.