Framing, Coalition Dynamics, and Legislative Outcomes in State-Level Artificial Intelligence Policy

• In Event: AI Policy, Law, and Regulatory Diffusion

Abstract

The rapid emergence of artificial intelligence (AI) as a transformative general-purpose technology has generated widespread scholarly, regulatory, and political attention. Policymakers increasingly face pressure to respond to the societal, economic, and ethical implications of AI systems, including misinformation, algorithmic bias, labor displacement, and systemic risk. These developments have elevated AI from a technical domain into a central object of governance and intensified calls for regulatory intervention. Within the United States, AI regulation is emerging as a multi-level governance challenge. State governments are playing an increasingly prominent role due to federal fragmentation, creating opportunities for policy innovation while also generating variation in regulatory approaches. California occupies a central position in this landscape. Its concentration of technology firms and research institutions gives it both the capacity and incentive to lead AI governance. However, state-level AI governance in the United States remains understudied despite its growing policy relevance. This paper proposes a comparative analysis of California AI bills with different legislative outcomes, including SB 1047 (2024), which was vetoed, and SB 53 (2025), which was enacted, to examine how stakeholder coalitions and competing problem framings shape legislative outcomes. The study situates AI policymaking within broader science, technology, and innovation policy frameworks, focusing on how actors construct and contest interpretations of AI-related risks and opportunities. The analysis draws on qualitative evidence from legislative texts, committee hearings, amendment histories, stakeholder submissions, and gubernatorial statements, and incorporates process tracing. It also uses text analysis techniques to systematically examine this qualitative material across documents, tracing how framing and coalition dynamics evolve over time. It assesses how competing narratives emphasize economic competitiveness and technological development on one hand, and ethical risks, transparency, and societal protection on the other, and how these framings relate to policy design and political feasibility. The study extends existing research into underexplored state-level processes by introducing a structured empirical approach to AI policymaking. It aims to clarify how framing and coalition dynamics interact with institutional context to influence the development of emerging technology regulation.

Author

• Adetunji Omole, University of North Carolina, Charlotte
  Presenting Author