AI Policy Models in China and the United States: A Multi-Level Comparative Analysis of Policy Convergence and Divergence

This article reports on a multi-level, mixed-methods comparative analysis of AI policy models in China and the United States, examining both divergence and convergence in their policy goals, implementation strategies, and implementation processes. While prior research highlights policy divergence, this article argues that convergence is equally crucial for cross-national policy learning. The study revealed that AI policies primarily diverge at the national level and converge at the local level. Drawing on theories of incrementalism, the fragmented authoritarianism model, path dependence, and mechanisms for policy convergence, this article offers a theoretical explanation for these dynamics, providing insights into and suggestions for the evolving landscape of global AI governance.