AI GOVERNANCE: A CHALLENGE FOR GLOBAL COOPERATION

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology with enormous potential across societies, economies, and political systems globally. However, with its rapid advancement come serious risks and governance challenges that transcend national borders. This paper examines AI Governance as a central arena in which global cooperation is urgently needed but difficult to achieve. We argue that AI governance involves not only regulation of technology but also ethical norms, risk management, accountability mechanisms, and international institutions to harmonize divergent national interests. Key tensions arise from geopolitical competition, variance in regulatory capacity, digital sovereignty concerns, ethical pluralism, and uneven distribution of technological power.

This research investigates the following questions: What are the main challenges to global cooperation in AI governance? What models, frameworks, and policies exist or are emerging to address these challenges? And what pathways can enable more effective international cooperation? To answer these questions, the paper draws on recent literature—including systematic literature reviews, policy analyses, institutional reports, and comparative scholarship—to map both the problems and the promising solutions.

In summary, global cooperation in AI governance faces five interrelated challenges: (1) fragmentation—multiple overlapping and inconsistent governance regimes; (2) geopolitical rivalry—states perceiving AI as a competitive advantage, sometimes at odds with cooperation; (3) capacity gaps—some states lack institutional, technical, or regulatory infrastructure; (4) ethical and value pluralism—differences in views on privacy, human rights, acceptable risk, algorithmic bias; (5) enforcement and accountability—how to ensure compliance, transparency, redress mechanisms across borders. On the positive side, several models show promise: multilateral treaties or conventions, soft‐law instruments (ethical guidelines, codes of practice), multistakeholder governance frameworks, global standard‐setting bodies, capacity-building and knowledge sharing platforms.

We propose a set of pathways toward more effective global cooperation in AI governance: developing a binding international framework focused on high-risk AI systems; strengthening existing multilateral institutions (UN, OECD, UNESCO) to take more central roles; establishing shared norms for transparency, fairness, accountability; investing in capacity building in less developed states; creating mechanisms for cross‐border enforcement and monitoring; promoting multi-stakeholder participation including civil society and private sector. The conclusion underscores that AI governance is not just a technical or regulatory issue but a political one—with issues of power, justice, and global equity. Only through cooperative, inclusive, ethical governance can AI’s benefits be maximized while minimizing risks.