Artificial Intelligence Governance Frameworks: A Comparative Study of Rights‑Based, Market‑Driven, and State‑Led Models

Abstract

The rapid advancement of artificial intelligence has prompted diverse regulatory responses across major global jurisdictions, yet the fragmentation of AI governance frameworks creates complex challenges for innovation, compliance, and international cooperation. This paper provides a comparative analysis of AI governance frameworks, examining three distinct regulatory models: the rights‑based approach, the market‑driven approach, and the state‑led approach. The analysis draws on primary legal and policy documents from the European Union, the United States, and China, supplemented by peer‑reviewed literature and policy analyses. The findings reveal that while all three models share common goals of balancing innovation with risk mitigation, significant divergences emerge in legal instruments, enforcement mechanisms, and fundamental values. The rights‑based model prioritizes fundamental rights and adopts a precautionary logic, the market‑driven model emphasizes innovation with post‑hoc oversight, and the state‑led model focuses on social stability and governmental control. These differences create a fragmented global landscape that poses practical challenges for multinational enterprises and underscores the urgent need for international dialogue and interoperability mechanisms in AI governance.