IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/394mj_v1.html

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: 10.31235/osf.io/394mj_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/6a5a4c2f04720f912c025eb1/
    Download Restriction: no
    File URL: https://libkey.io/10.31235/osf.io/394mj_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Parinandi, Srinivas & Crosson, Jesse & Peterson, Kai & Nadarevic, Sinan, 2024. "Investigating the politics and content of US State artificial intelligence legislation," Business and Politics, Cambridge University Press, vol. 26(2), pages 240-262, June.
    3. Craig Volden, 2006. "States as Policy Laboratories: Emulating Success in the Children's Health Insurance Program," American Journal of Political Science, John Wiley & Sons, vol. 50(2), pages 294-312, April.
    4. Ornstein, Joseph T. & Blasingame, Elise N. & Truscott, Jake S., 2025. "How to train your stochastic parrot: large language models for political texts," Political Science Research and Methods, Cambridge University Press, vol. 13(2), pages 264-281, April.
    5. Pedro Robles & Daniel J. Mallinson, 2025. "Artificial intelligence technology, public trust, and effective governance," Review of Policy Research, Policy Studies Organization, vol. 42(1), pages 11-28, January.
    6. repec:cup:apsrev:v:63:y:1969:i:03:p:880-899_25 is not listed on IDEAS
    7. Fabrizio Gilardi & Charles R. Shipan & Bruno Wüest, 2021. "Policy Diffusion: The Issue‐Definition Stage," American Journal of Political Science, John Wiley & Sons, vol. 65(1), pages 21-35, January.
    8. Charles R. Shipan & Craig Volden, 2008. "The Mechanisms of Policy Diffusion," American Journal of Political Science, John Wiley & Sons, vol. 52(4), pages 840-857, October.
    9. SHOR, BORIS & McCARTY, NOLAN, 2011. "The Ideological Mapping of American Legislatures," American Political Science Review, Cambridge University Press, vol. 105(3), pages 530-551, August.
    10. repec:cup:apsrev:v:63:y:1969:i:03:p:880-899_00 is not listed on IDEAS
    11. Berry, Frances Stokes & Berry, William D., 1990. "State Lottery Adoptions as Policy Innovations: An Event History Analysis," American Political Science Review, Cambridge University Press, vol. 84(2), pages 395-415, June.
    12. Caughey, Devin & Warshaw, Christopher, 2018. "Policy Preferences and Policy Change: Dynamic Responsiveness in the American States, 1936–2014," American Political Science Review, Cambridge University Press, vol. 112(2), pages 249-266, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arbolino, Roberta & Boffardi, Raffaele & Bonasia, Mariangela & Capasso, Salvatore & De Simone, Luisa, 2025. "Imitation or learning: Exploring the drivers of Special economic zones," Structural Change and Economic Dynamics, Elsevier, vol. 72(C), pages 456-466.
    2. Amy Y. Li, 2017. "Covet Thy Neighbor or “Reverse Policy Diffusion”? State Adoption of Performance Funding 2.0," Research in Higher Education, Springer;Association for Institutional Research, vol. 58(7), pages 746-771, November.
    3. Felix Strebel & Thomas Widmer, 2012. "Visibility and facticity in policy diffusion: going beyond the prevailing binarity," Policy Sciences, Springer;Society of Policy Sciences, vol. 45(4), pages 385-398, December.
    4. Armin Mertens & Christine Trampusch & Florian Fastenrath & Rebecca Wangemann, 2021. "The political economy of local government financialization and the role of policy diffusion," Regulation & Governance, John Wiley & Sons, vol. 15(2), pages 370-387, April.
    5. Fabrizio Gilardi, 2010. "Who Learns from What in Policy Diffusion Processes?," American Journal of Political Science, John Wiley & Sons, vol. 54(3), pages 650-666, July.
    6. Evan M. Mistur & John Wagner Givens & Daniel C. Matisoff, 2023. "Contagious COVID‐19 policies: Policy diffusion during times of crisis," Review of Policy Research, Policy Studies Organization, vol. 40(1), pages 36-62, January.
    7. Yang, Ju-Ying, 2025. "Pathways to a sustainable energy future: A comparative case study of four Texas municipalities," Energy Policy, Elsevier, vol. 204(C).
    8. Derek Glasgow & Shuang Zhao & Saatvika Rai, 2021. "Rethinking Climate Change Leadership: An Analysis of the Ambitiousness of State GHG Targets," Review of Policy Research, Policy Studies Organization, vol. 38(4), pages 398-426, July.
    9. Yunxiang Zhang & Shichen Wang, 2021. "How does policy innovation diffuse among Chinese local governments? A qualitative comparative analysis of River Chief Innovation," Public Administration & Development, Blackwell Publishing, vol. 41(1), pages 34-47, February.
    10. Nico Heiden & Felix Strebel, 2012. "What about non-diffusion? The effect of competitiveness in policy-comparative diffusion research," Policy Sciences, Springer;Society of Policy Sciences, vol. 45(4), pages 345-358, December.
    11. Armstrong, John H., 2019. "Modeling effective local government climate policies that exceed state targets," Energy Policy, Elsevier, vol. 132(C), pages 15-26.
    12. Min Xiong & Shaoming Cheng & Jerry Zhirong Zhao, 2025. "Policy Imitation Based on Similarities: The Diffusion of Public–Private Partnerships," Public Administration & Development, Blackwell Publishing, vol. 45(2), pages 130-145, May.
    13. Valente, Thomas W. & Pitts, Stephanie & Wipfli, Heather & Vega Yon, George G., 2019. "Network influences on policy implementation: Evidence from a global health treaty," Social Science & Medicine, Elsevier, vol. 222(C), pages 188-197.
    14. Srinivas C. Parinandi, 2020. "Policy Inventing and Borrowing among State Legislatures," American Journal of Political Science, John Wiley & Sons, vol. 64(4), pages 852-868, October.
    15. Chun-Fang Chiang & Jason M. Kuo & Megumi Naoi & Jin-Tan Liu, 2020. "What Do Voters Learn from Foreign News? Emulation, Backlash, and Public Support for Trade Agreements," NBER Working Papers 27497, National Bureau of Economic Research, Inc.
    16. Xiaohan Li & Yang Lv & Md Nazirul Islam Sarker & Xun Zeng, 2022. "Assessment of Critical Diffusion Factors of Public–Private Partnership and Social Policy: Evidence from Mainland Prefecture-Level Cities in China," Land, MDPI, vol. 11(3), pages 1-15, February.
    17. Gregor Schwerhoff, 2016. "The economics of leadership in climate change mitigation," Climate Policy, Taylor & Francis Journals, vol. 16(2), pages 196-214, March.
    18. Weixing Liu & Hongtao Yi, 2020. "What Affects the Diffusion of New Energy Vehicles Financial Subsidy Policy? Evidence from Chinese Cities," IJERPH, MDPI, vol. 17(3), pages 1-15, January.
    19. Aina Gallego & Gaël Le Mens & Nikolas Schöll, 2021. "Politician-Citizen Interactions and Dynamic Representation: Evidence from Twitter," Working Papers 1238, Barcelona School of Economics.
    20. Wang, Ryan Yang & Jayakar, Krishna, 2024. "Learning from the neighbors: The diffusion of state broadband policies in the United States," Telecommunications Policy, Elsevier, vol. 48(7).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:socarx:394mj_v1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://socarxiv.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.