Algorithmic Governance in the United States: A Multi-Level Case Analysis of AI Deployment Across Federal, State, and Municipal Authorities
Maxim Dedyaev
TL;DR
The paper analyzes how AI is embedded across three levels of U.S. government—federal, state, and municipal—to reveal a structured, level-dependent regime of algorithmic governance. It adopts a comparative qualitative framework grounded in the third wave of digital-era governance and sociotechnical theory to classify AI deployments into control-oriented and support-oriented regimes. The main contributions include documenting systematic functional differentiation by level, linking governance forms to accountability and risk, and offering a framework for level-sensitive AI governance policy. The study's findings have practical implications for regulating AI in the public sector, vendor governance, and data stewardship, illustrating how identical technologies can produce distinct governance outcomes depending on institutional embedding.
Abstract
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.