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Regulate, Promote, or Study? Measuring the Policy Orientation of U.S. State Artificial Intelligence Legislation

Author

Listed:
  • Langehennig, Stefani

    (University of Denver)

  • Roney, Dani

Abstract

State legislatures have become the primary venue for artificial intelligence (AI) poli- cymaking in the United States, but existing measures of state AI activity count bills without distinguishing what those bills actually do. This research note introduces a new measure of the policy orientation of state AI legislation. Drawing on a corpus of approximately 1.45 million state bills, we identify 3,124 high-confidence AI bills using a validated two-tier keyword system benchmarked against the National Conference of State Legislatures’ AI legislation database. We then classify each bill as regulatory, promotional, or procedural using a large language model with human validation. Regu- latory bills dominate the agenda (61.7%), but one in four AI bills is purely procedural: task forces, studies, and reports that create no substantive policy. The measure sep- arates legislative activity from governance commitment and provides a resource for research on technology federalism, policy diffusion, and symbolic politics.

Suggested Citation

  • Langehennig, Stefani & Roney, Dani, 2026. "Regulate, Promote, or Study? Measuring the Policy Orientation of U.S. State Artificial Intelligence Legislation," SocArXiv 394mj_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:394mj_v1
    DOI: 10.31219/osf.io/394mj_v1
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