Abstract
This Article presents a taxonomy and a framework for the emerging AI governance system. The range of solutions and approaches between softer and harder, collaborative and adversarial private-public regulation, is neither binary nor static but rather dynamic and varied across contexts. AI regulation necessitates a multifaceted approach as well as attention to skill-building, market competition, and infrastructure. A balanced, rational debate about the costs and benefits, risks and potential of AI is crucial to ensuring that regulators are attentive to the entire range within their regulatory toolbox and are open to experimentation, research, and investment in AI for good.
Recommended Citation
Orly Lobel,
The AI Regulatory Pyramid: A Taxonomy & Analysis of the Emerging Toolbox in the Global Race for the Regulation and Governance of Artificial Intelligence,
57 Loy. L.A. L. Rev. 859
(2025).
Available at: https://digitalcommons.lmu.edu/llr/vol57/iss4/1
