Comparing Apples to Oranges: A Taxonomy for Navigating the Global Landscape of AI Regulation
Sacha Alanoca, Shira Gur-Arieh, Tom Zick, Kevin Klyman
TL;DR
The paper presents a taxonomy to navigate the global AI regulation landscape, addressing semantic ambiguity between soft and hard law and the risk of fragmentation. By applying eleven measurable metrics across five major jurisdictions (EU, US, Canada, China, Brazil) and providing data-driven visualizations, it offers a comparable, globally inclusive view of how regulations differ in scope, maturity, enforcement, and stakeholder engagement. The approach emphasizes ex ante versus ex post governance, horizontal versus vertical regulatory logic, and technology- versus application-focused regulation to map patterns and gaps. The work aims to reduce regulatory uncertainty, support evidence-based policymaking, and foster more inclusive, globally coordinated AI governance, while acknowledging the need for ongoing updates as regulation evolves across time.
Abstract
AI governance has transitioned from soft law-such as national AI strategies and voluntary guidelines-to binding regulation at an unprecedented pace. This evolution has produced a complex legislative landscape: blurred definitions of "AI regulation" mislead the public and create a false sense of safety; divergent regulatory frameworks risk fragmenting international cooperation; and uneven access to key information heightens the danger of regulatory capture. Clarifying the scope and substance of AI regulation is vital to uphold democratic rights and align international AI efforts. We present a taxonomy to map the global landscape of AI regulation. Our framework targets essential metrics-technology or application-focused rules, horizontal or sectoral regulatory coverage, ex ante or ex post interventions, maturity of the digital legal landscape, enforcement mechanisms, and level of stakeholder participation-to classify the breadth and depth of AI regulation. We apply this framework to five early movers: the European Union's AI Act, the United States' 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 offer an interactive visualization that distills these dense legal texts into accessible insights, highlighting both commonalities and differences. By delineating what qualifies as AI regulation and clarifying each jurisdiction's approach, our taxonomy reduces legal uncertainty, supports evidence-based policymaking, and lays the groundwork for more inclusive, globally coordinated AI governance.