AI Governance

Chen, Yiying; Chen, Tong. "The Grammar of Diffusion: How Policy Formality Shapes the Spread of AI Governance Across U.S. States"

Status: Working Paper. Presented at Association for Public Policy Analysis and Management (APPAM) Annual Fall Research Conference. Seattle, WA. November 13-15, 2025.

Abstract: This study examines how the internal structure of policy design influences the diffusion of artificial intelligence (AI) governance across U.S. states. Applying institutional grammar theory, it investigates whether higher levels of policy formality enhance diffusion effectiveness and efficiency, and whether different types of prescriptions shape diffusion outcomes. To support this analysis, the study constructs a dataset of more than 2,000 state-level AI policies from all 50 states and uses machine learning and generative AI to classify and differentiate their substantive focuses rather than treating AI legislation as a single, uniform category. Event history models assess how AIC strategies, ADIC norms, and ADICO rules relate to patterns of policy spread. The findings advance diffusion scholarship by revealing how micro-level design choices influence macro-level adoption and offer practical insights for crafting clearer, more transferable AI governance frameworks.