The rapid expansion of artificial intelligence in public governance has generated strong optimism about faster processes, smarter decisions, and more modern administrative systems. Yet despite this enthusiasm, we still know surprisingly little about how AI actually takes shape inside different layers of government. Especially in federal systems where authority is fragmented across multiple levels. In practice, the same algorithm can serve very different purposes. This study responds to that gap by examining how AI is used across federal, state, and municipal levels in the United States. Drawing on a comparative qualitative analysis of thirty AI implementation cases, and guided by a digital-era governance framework combined with a sociotechnical perspective, the study identifies two broad modes of algorithmic governance: control-oriented systems and support-oriented systems. The findings reveal a clear pattern of functional differentiation across levels of government. At the federal level, AI is most often institutionalized as a tool for high-stakes control: supporting surveillance, enforcement, and regulatory oversight. State governments occupy a more ambiguous middle ground, where AI frequently combines supportive functions with algorithmic gatekeeping, particularly in areas such as welfare administration and public health. Municipal governments, by contrast, tend to deploy AI in more pragmatic and service-oriented ways, using it to streamline everyday operations and improve direct interactions with residents. By foregrounding institutional context, this study advances debates on algorithmic governance by demonstrating that the character, function, and risks of AI in the public sector are fundamentally shaped by the level of governance at which these systems are deployed.

翻译：人工智能在公共治理领域的迅速扩展引发了对其加速流程、优化决策及推动行政系统现代化的强烈乐观预期。然而，尽管热情高涨，我们对于人工智能在不同政府层级中的实际运作机制仍知之甚少，尤其是在权力分散于多层级的联邦体系中。实践中，同一算法可能服务于截然不同的治理目标。本研究通过考察美国联邦、州及市政三个层级的人工智能应用情况，试图填补这一认知空白。基于对三十个AI实施案例的比较质性分析，并依托数字时代治理框架与社会技术视角的双重理论指导，本研究识别出算法治理的两种基本模式：控制导向型系统与支持导向型系统。研究发现揭示了政府层级间清晰的功能分化模式：在联邦层面，人工智能通常被制度化为高风险控制的工具，用于支持监控、执法与监管审查；州政府则处于更为模糊的中间地带，其AI应用常将支持性功能与算法把关机制相结合，尤其在福利管理与公共卫生等领域；相比之下，市政政府倾向于以更务实和服务导向的方式部署AI，通过优化日常运营与改善居民直接互动来提升治理效能。本研究通过突出制度情境的重要性，论证了公共部门中人工智能的特性、功能与风险根本上受其部署层级的塑造，从而推动了关于算法治理的学术讨论。