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.

中文翻译：

---

推动公民对政府人工智能认知的制度因素：来自警务调查实验的证据

执法机构越来越多地采用人工智能 (AI) 驱动的工具。虽然之前的工作强调推动公众舆论的技术特征，但我们调查了公众对政府人工智能的信任和支持如何随着制度背景的变化而变化。我们对 4200 名受访者进行了一项关于警务中人工智能用例的预先注册调查实验，以衡量对三个关键制度因素的反应：官僚接近度（即地方警长与国家联邦调查局）、算法目标（即通过预测的公共目标）警务与通过自动案件审查检测官员不当行为）以及机构能力（即必要的资源和专业知识）。我们发现，与国家执法部门相比，公众显然更喜欢地方执法机构使用人工智能，而对不同算法目标的反应则更加有限和政治化。然而，我们没有发现对机构能力或缺乏能力的反应。研究结果表明，学术界、从业者和公众需要更多地关注政府成功实施人工智能的组织先决条件，而不仅仅是技术先决条件。