Conference Agenda

Local governments across the United States have emerged as frontline regulators of artificial intelligence (AI) [1]. They have adopted bans on facial recognition, internal AI-use guidelines, procurement rules, registries, and oversight bodies. In parallel, all 50 U.S. States and several territories introduced AI legislation in 2025, with 100 bills adopted [2]. In contrast, federal action has largely stalled, with no stand-alone AI regulation enacted despite more than 150 bills being introduced in the 118th and 119th Congress [3, 4]. Rather than reflecting mere bureaucratic inefficiencies, this fragmented landscape points to deeper structural dynamics in U.S. governance and political economy that hinder national AI regulation.

Local AI governance is a form of community-level contestation of emerging technologies and, in many ways, is a response to federal inaction and rising populism. Yet, we know relatively little about how local governments approach AI in practice and what values are embedded in their regulatory efforts. Accordingly, through content analysis, this paper answers the following research questions:

RQ1: What domains and applications of AI are targeted by local government policies?
RQ2: What values, priorities, and motivations are reflected in local government policies regulating AI in the United States?

The paper draws on two theoretical perspectives: AI localism and counter‑imaginaries. Localism refers broadly to governance arrangements that shift authority and decision‑making away from centralized institutions toward actors embedded in particular places [5]. Within this framework, Ira Rubinstein’s account of “privacy localism” has shown how cities regulate surveillance and data practices to fill gaps in federal privacy law, advancing contextual values and injecting democratic control [6]. AI localism extends the logic to algorithmic systems: it describes the actions taken by local decision‑makers to govern AI within a city or community [1].

Local AI policies function as both regulatory instruments and discursive artifacts that articulate how cities imagine alternative visions of techno-social life. Because local governance operates through proximate democratic processes and responds to community concerns, these policies can reveal situated priorities and values. As these priorities and values diverge from, and potentially subvert, dominant national or industry imaginaries, we refer to them as counter-imaginaries [7, 8, 9].

Empirically, the paper analyzes a corpus of 65 AI-related policies, directives, and executive orders enacted by U.S. local governments before October 2025. We identified documents through a systematic online search leveraging Google’s boolean operator. We then conducted qualitative content analysis with inductive sentence‑level coding framed around our RQs, with particular attention to the values, objectives, and priorities articulated in the text.

We use qualitative content analysis with inductive thematic coding because it allows us to identify and interpret recurring patterns of priorities and values across the policy documents, with flexibility to be guided by our research questions [10]. The first author developed an initial codebook using one‑third of the sample, and a second coder applied it to 10% of the data. Through calibration meetings, we refined a 29‑code codebook for which Krippendorff’s alphas exceeded 0.80 (excellent agreement) on all codes. The codebook offers a reusable framework for examining emerging AI policies in other jurisdictions.

Our analysis identifies three higher-order themes that speak to RQ2: (1) democratic oversight; (2) rights-based governance; and (3) contextual alignment. First, many local AI policies foreground democratic oversight as a core mechanism of AI governance. They require reporting on the purpose, scope, and functionality of AI systems and pair transparency obligations with mechanisms for public input, review, and contestation. In doing so, they articulate a right to know about and a right to speak on AI technologies in local public life, pointing to institutional designs for more participatory AI governance.

Second, many policies explicitly assert commitments to ethical, responsible, and safe AI use, grounded in rights-based frameworks. Many invoke digital, civil, human, and privacy rights as foundational principles and frame responsible AI through equity and nondiscrimination. This rights‑oriented stance anchors AI regulation in existing civil and human rights regimes and moves ethical governance toward enforceable obligation.

Third, local AI policies emphasize contextual alignment, framing regulation as an extension of existing civic commitments. In this way, they position emerging technologies within broader narratives of place-making and urban governance rather than treating AI as an expansive, one-size-fits-all infrastructure.

Together, these findings show how local governments not only fill regulatory gaps but also articulate alternative models for AI governance that could influence broader communications and technology policy debates. By surfacing community priorities and values around AI, this research provides an empirically grounded framework that policymakers and advocates can use to challenge dominant imaginaries and design more accountable, context‑sensitive AI governance.