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

arXiv: https://arxiv.org/abs/2505.13673 · HTML: https://mlsanity.com/paper/2505.13673/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2025.

## Built on by (most cited first, 7 of 7)

- [Generalizability of Large Language Model-Based Agents: A Comprehensive Survey](https://mlsanity.com/paper/2509.16330/) (2025)
- [AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance](https://mlsanity.com/paper/2510.01474/) (2025)
- [Computational Compliance for AI Regulation: Blueprint for a New Research Domain](https://mlsanity.com/paper/2601.04474/) (2026)
- [Now You See Me: Designing Responsible AI Dashboards for Early-Stage Health Innovation](https://mlsanity.com/paper/2602.23378/) (2026)
- [How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications](https://mlsanity.com/paper/2604.22679/) (2026)
- [Navigating Global AI Regulation: A Multi-Jurisdictional Retrieval-Augmented Generation System](https://mlsanity.com/paper/2604.25448/) (2026)
- [Visible to the Court: How AI Is (and Isn't) Litigated in U.S. Federal Court Opinions](https://mlsanity.com/paper/2607.23888/) (2026)

## Abstract (arXiv)

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.