Converging Goals and Local Choices Federal and State AI Policy in the United States

• In Event: Politics and Policymaking in State-Level AI Governance

Abstract

Federal and state governments can pursue similar AI policy goals while making different commitments about their implementation. This paper examines how those differences develop and how state circumstances help explain them. Combining policy-text analysis, state conditions, and formal provisions, we find that states became more similar in their goal composition and overall instrument use, while differences in the instruments attached to individual goals declined much less. Economic and public-spending structure was most informative about development support. Professional and research resources were associated with greater emphasis on safeguards across electoral settings, but states with similar resources still combined support and protection differently. Common goals leave consequential choices unresolved. Governments must identify which obstacles warrant intervention, organize the resources needed to address them, and determine who bears responsibility for risks. Research-support and assessment provisions show how these judgments give familiar instruments distinct purposes. Shared professional methods can spread through federal and interstate institutions while acquiring practical force through state authority. The resulting pattern is convergence in broad commitments alongside persistent differences in how governments pursue them. Understanding those differences requires examining the problems, responsibilities, and capabilities attached to a shared goal.

Authors

• Jiahui Zhang, Harbin Institute of Technology
  Presenting Author

• Tao Hong, Harbin Institute of Technology
  Non-Presenting Co-Author

• Zitong Tang, Northwest University of Political Science and Law
  Non-Presenting Co-Author