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

2025-05-19

AI Summary

Global AI regulation faces critical challenges—including ambiguous definitions, fragmented regulatory frameworks, and asymmetric information—exacerbating risks of public misinformation, impediments to international cooperation, and regulatory capture. To address these, this paper introduces the first systematic taxonomy for AI governance, structured along six analytical dimensions: technological vs. application-oriented focus; horizontal vs. sector-specific scope; ex ante vs. ex post intervention; bindingness; enforcement mechanism; and accountability architecture. We apply a mixed-methods approach—integrating qualitative policy analysis, cross-jurisdictional comparison, and structured coding—to standardize and map five landmark regulatory instruments, including the EU AI Act and U.S. Executive Order 14110. We further develop a novel multidimensional comparability framework and an interactive D3.js visualization tool. The taxonomy enhances regulatory transparency and cross-jurisdictional comparability, reduces legal uncertainty, and provides empirical and methodological foundations for embedding democratic values and advancing global regulatory coordination.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: AI for Accessibility
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Responsible Web: Technology governance, policy, and regulations
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Clarifying blurred definitions of AI regulation to prevent public misinformation
• Addressing divergent regulatory frameworks to avoid international cooperation fragmentation
• Reducing uneven access to key information to mitigate regulatory capture risks

Innovation

• Develops a taxonomy for global AI regulation classification
• Uses interactive visualization for legal text insights
• Applies framework to five major regulatory jurisdictions