[Paper Review] The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research

Jonas Tallberg, Eva Erman+4 more|arXiv (Cornell University)|May 19, 2023

International Arbitration and Investment Law4 citations

TL;DR

This paper proposes a dual research agenda for the global governance of artificial intelligence, combining empirical mapping of existing regulatory architectures with normative frameworks for evaluating their legitimacy and effectiveness. It advances both scholarly understanding and policy-relevant standards for governing AI at the global level.

ABSTRACT

Artificial intelligence (AI) represents a technological upheaval with the potential to change human society. Because of its transformative potential, AI is increasingly becoming subject to regulatory initiatives at the global level. Yet, so far, scholarship in political science and international relations has focused more on AI applications than on the emerging architecture of global AI regulation. The purpose of this article is to outline an agenda for research into the global governance of AI. The article distinguishes between two broad perspectives: an empirical approach, aimed at mapping and explaining global AI governance; and a normative approach, aimed at developing and applying standards for appropriate global AI governance. The two approaches offer questions, concepts, and theories that are helpful in gaining an understanding of the emerging global governance of AI. Conversely, exploring AI as a regulatory issue offers a critical opportunity to refine existing general approaches to the study of global governance.

Motivation & Objective

• To address the growing need for systematic research on global AI governance, which has so far been overshadowed by studies on AI applications.
• To identify and analyze the emerging institutional, normative, and regulatory architectures shaping global AI governance.
• To bridge the gap between empirical observation of global AI governance mechanisms and normative evaluation of their legitimacy and effectiveness.
• To refine general theories of global governance by treating AI as a critical case study for institutional and normative innovation.
• To stimulate interdisciplinary research that integrates insights from political science, international relations, and ethics in the context of AI regulation.

Proposed method

• Employing an empirical approach to map and analyze the structure, actors, and mechanisms of global AI governance, including multilateral, plurilateral, and transgovernmental initiatives.
• Applying normative frameworks—such as legitimacy, accountability, and equity—to evaluate the fairness and effectiveness of global AI governance arrangements.
• Drawing on theories of global regulatory governance, including multilevel and networked governance, to interpret the evolving landscape of AI regulation.
• Conducting comparative case studies of key global AI governance initiatives (e.g., OECD, UNESCO, EU AI Act, UN initiatives) to identify patterns and divergences.
• Integrating qualitative and conceptual analysis to assess the normative foundations of proposed governance models.
• Using a dual-methodological framework to ensure that empirical findings inform normative standards and vice versa, fostering iterative scholarly and policy engagement.

Experimental results

Research questions

• RQ1 What are the key institutional and normative architectures shaping global AI governance, and how do they interact?
• RQ2 How do existing global AI governance mechanisms perform in terms of legitimacy, transparency, and inclusivity?
• RQ3 In what ways can normative standards be developed to guide the design and implementation of effective global AI governance?
• RQ4 How does the governance of AI challenge or refine established theories of global regulatory governance?
• RQ5 What role do non-state actors, such as tech firms and civil society, play in shaping the global AI governance architecture?

Key findings

• Global AI governance is increasingly characterized by a complex, multi-layered architecture involving international organizations, regional blocs, and non-state actors.
• There is a growing divergence in regulatory approaches between regions such as the EU, the US, and China, reflecting differing normative priorities and institutional capacities.
• Current governance mechanisms often lack sufficient inclusivity and transparency, particularly in representing the interests of Global South countries.
• The normative foundations of global AI governance remain underdeveloped, with limited consensus on principles such as fairness, accountability, and human rights protection.
• Existing empirical studies on AI governance are still in early stages, with insufficient attention to institutional design and implementation dynamics.
• The integration of empirical and normative research offers a powerful pathway to strengthen both scholarly analysis and policy relevance in AI governance.