Beyond Regulatory Fragmentation: A Taxonomy for Navigating Global AI Regulation

AI governance has transitioned from soft law—such as national strategies and voluntary guidelines—to binding regulation at an unprecedented pace. This evolution has produced a complex legislative landscape: divergent frameworks complicate international cooperation, blurred definitions of “AI regulation” can mislead public expectations, and uneven access to key information heightens the risk of regulatory capture. Clarifying the scope and substance of AI regulation is vital. We present a concise taxonomy for evaluating emerging AI regulations. Our approach targets essential metrics—technology or application-focused rules, horizontal or sectoral regulatory coverage, ex ante or ex post regulatory approaches, enforcement mechanisms, and stakeholder participation—to classify the breadth and depth of AI regulation. We apply this taxonomy to five early movers: the EU AI Act, the U.S. Executive Order 14110, Canada’s AI and Data Act, China’s Interim Measures for Generative AI Services, and Brazil’s AI Bill 2338/2023. We further develop an interactive visualization to synthesize these dense legal texts into clear, actionable insights, illuminating both common ground and significant gaps.

What you will learn:

• Clarifying the confusion around the loose semantic use of “AI regulation” and the risks in misleading the public about the true scope and strength of regulatory measures.
• How to avoid framing innovation versus regulation into a false dichotomy.
• The combination of different regulatory approaches, with different weights.