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

Authors

  • Sacha Alanoca Stanford, OECD
  • Shira Gur-Arieh Harvard Law School, Harvard University
  • Tom Zick Berkman Klein Center, Harvard University
  • Kevin Klyman Freeman Spogli Institute for International Studies, Stanford University

DOI:

https://doi.org/10.70777/si.v2i3.15137

Keywords:

AI governance, AI regulation, AI ethics, artificial general intelligence, responsible AI, participatory AI, risks of regulatory capture, agi governance, artificial intelligence governance

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.

Author Biographies

Sacha Alanoca, Stanford, OECD

Sacha is a Senior AI Policy Researcher and Head of Community Development at The Future Society, where she manages projects and research centered on the governance, policy and ethics of AI systems. Her research ranges from the design of national AI policies, to the development of independent audits for AI systems, and civic empowerment platforms such as the AI Civic Forum. Sacha recently led the publication “Responsible AI in Pandemic Response” for the Global Partnership on AI (GPAI) and is currently working with the Tunisian government and GIZ to elaborate Tunisia’s AI National Strategy.

Sacha also leads our community development composed of over 60 Advisors and Affiliates from leading academic, policy and industry backgrounds, working on topics such as AI national strategies, AI for development, algorithmic bias or existential risks.

Previously, Sacha worked at the OECD Development Center in Paris, and at two leading think tanks in Brazil and Chile. Sacha is Franco-Chilean.

Shira Gur-Arieh, Harvard Law School, Harvard University

Essays On Legitimacy in Machine-Learning Algorithms

My research examines questions that relate to legitimacy in machine-learning algorithms, and addresses whether they meet minimal conditions to deserve compliance from their subjects. Many of these questions originate in the ways in which algorithmic prediction meaningfully departs from traditional, human decision-making processes. Predictive algorithms detect patterns from historical data and apply them to individuals, with the narrow goal of maximizing predictive accuracy. Humans rarely maximize accuracy at all costs, but rather incorporate a range of values when making decisions, such as merit, desert, agency and equality. While humans typically pay attention to the distinctive aspects of each individual case, a predictive algorithm will infer an individual’s future behavior from the historical behavior of his statistical peers. Humans tend to ground their decisions in logical and intelligible explanations, whereas algorithmic predictions are driven by statistical correlations, which may lack intuitive support. For these reasons and others, good predictions do not necessarily lead to good decisions, or to otherwise just and desirable outcomes. This gap between machine predictions and human decisions is not always challenged by existing notions of fairness. In my dissertation, I shed light on structural features of machine-learning algorithms, and analyze how they may undermine the fundamental legitimacy of their use.

Tom Zick, Berkman Klein Center, Harvard University

Tom Zick earned her PhD from UC Berkeley and is currently pursuing her JD at Harvard. Her research bridges between AI ethics and law, with a focus on how to craft safe and equitable policy surrounding the adoption of AI in high-stakes domains. In the past, she has worked as a data scientist at the Berkeley Center for Law and Technology, evaluating the capacity of regulations to promote open government data. She has also collaborated with graduate students across social science and engineering to advocate for pedagogy reform focused on infusing social context into technical coursework. Outside of academia, Tom has crafted digital policy for the City of Boston as a fellow for the Mayor’s Office for New Urban Mechanics and developed responsible AI resources for founders as a VC fellow at Bloomberg BETA. Her current research centers on the near term policy concerns surrounding reinforcement learning.

Kevin Klyman, Freeman Spogli Institute for International Studies, Stanford University

Kevin Klyman is a technology policy strategist focused on artificial intelligence, U.S.-China competition, and regulating emerging technologies. In addition to being an MIP candidate at Stanford, he is a Technology Policy Researcher at Harvard’s Avoiding Great Power War Project, an Emerging Expert at the Forum on the Arms Trade, and a prospective JD candidate at Harvard Law School.

Klyman’s writing on the technology and geopolitics has been published in Foreign Policy, TechCrunch, Just Security, The American Prospect, The Diplomat, Inkstick, The National Interest, and South China Morning Post. He is the author of “The Great Tech Rivalry: China vs. the U.S.” with Professor Graham Allison, which has been cited by The Wall Street Journal, The Economist, and NPR among others.

Klyman’s research primarily addresses responsible development and use of large AI models in the United States, Europe, and China. He also conducts research related to compute governance, quantum computing export controls, telecommunications infrastructure deployment, clean energy supply chains, biotechnology supply chains, digital trade agreements, digital technology regulators, and digital development institutions.

Klyman has led tech policy initiatives for a variety of the world’s leading international organizations. As an Artificial Intelligence and Digital Rights Fellow at United Nations Global Pulse, the AI lab of the UN Secretary-General, he headed the organization’s work on national AI strategies and coordinated the UN’s Privacy Policy Group. Klyman helped lead the development of a risks, harms, and benefits assessment for algorithmic systems that is now used across the UN. His other projects included working with engineers to address risks posed by the UN’s machine learning-based tools, organizing international consultations on data governance frameworks, and drafting data sharing agreements between the UN and the private sector. After the onset of the pandemic, Klyman coauthored a new privacy policy in partnership with the World Health Organization—the “Joint Statement on Data Protection and Privacy in the COVID-19 Response”—which was adopted by the UN as a whole.

As a Policy Fellow at the UN Foundation’s Digital Impact Alliance, Klyman built a database that is now used by the World Bank and the UN Development Programme to assess countries' readiness for digital investment. He also worked with the German and Estonian governments to spin up the GovStack initiative in order to assist governments in providing digital services. At the Campaign to Stop Killer Robots, Klyman directed research on countries’ policies regarding autonomous weapons, resulting in the landmark report “Stopping Killer Robots: Country Positions on Banning Fully Autonomous Weapons and Retaining Human Control.”

Klyman has also contributed to a number of policy arenas aside from technology. At Human Rights Watch, he helped expose war crimes in Syria and Yemen through open-source intelligence gathering and coauthored a report about the illegal use of cluster munitions. As a Legislative Assistant to the Mayor of Berkeley, California, he drafted a dozen pieces of legislation that nearly doubled the city’s investments in affordable housing. Additionally, as a Legislative Assistant to an elected commissioner on the Berkeley Rent Stabilization Board, he authored enabling legislation that paved the way for Berkeley to become one of the first and only cities in the country to ban housing discrimination against formerly incarcerated tenants.

Klyman attended UC Berkeley as an undergraduate, graduating with highest honors in political science along with a degree in applied mathematics concentrating in computer science. He is an award-winning debater who achieved the highest ranking in Berkeley’s history in American parliamentary debate and was Co-President of Berkeley’s parliamentary debate team; he has also coached multiple national debate champions. His thesis on Chinese foreign policy won the Owen D. Young Prize as the top paper in international relations and he received the John Gardner Public Service Fellowship as one of Berkeley’s top three public service-oriented graduates. He serves as Co-President of the John Gardner Fellowship Association, a 501(c)3.

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AI Governance Timeline 2025 - 2030+

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Published

2025-07-20

How to Cite

Alanoca, S., Gur-Arieh, S., Zick, T., & Klyman, K. (2025). Comparing Apples to Oranges: A Taxonomy for Navigating the Global Landscape of AI Regulation. SuperIntelligence - Robotics - Safety & Alignment, 2(3). https://doi.org/10.70777/si.v2i3.15137

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Vol. 2 No. 3 (2025): Governance, Agents, Evolutionary Search

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Copyright (c) 2025 Sacha Alanoca, Shira Gur-Arieh, Tom Zick, Kevin Klyman

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