Comparing Apples to Oranges: A Taxonomy for Navigating the Global Landscape of AI Regulation

TL;DR

This paper introduces a taxonomy to systematically categorize and compare global AI regulations, helping clarify legal landscapes and support better international coordination and policymaking.

Contribution

It presents a novel framework for classifying AI regulation based on key metrics, applied to major jurisdictions, with an interactive visualization tool.

Findings

Highlights differences and similarities in AI regulation across five countries.

Provides a clear classification scheme to reduce legal uncertainty.

Supports evidence-based policymaking and international cooperation.

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.