Global, selective, or both? The case for differentiated cooperation in AI governance

Area of Science:

• Political Science
• Computer Science
• International Relations

Background:

• Current discussions on international AI governance are often oversimplified, polarized between global cooperation and alliances of like-minded nations.
• This binary approach fails to address the complexity of governing artificial intelligence effectively.

Purpose of the Study:

• To propose a more systematic and nuanced framework for cooperative AI governance.
• To move beyond simplistic global vs. selective cooperation debates.
• To guide policymakers in selecting appropriate collaboration strategies for AI governance.

Main Methods:

• Analysis of AI governance challenges based on three key factors: the nature of the governance issue, the type of AI system, and the AI lifecycle phase (development vs. deployment).
• Integration of these factors to determine the optimal scope of international cooperation.

Main Results:

• Different AI governance issues require different forms of cooperation; not all AI presents the same challenges.
• The phase of AI development or deployment influences the necessary cooperative approach.
• Global cooperation is essential for some challenges, while selective cooperation is more effective for others.

Conclusions:

• Policymakers should adopt a flexible approach to international AI governance, choosing the scope of collaboration (global or selective) that best suits the specific issue.
• Moving beyond a one-size-fits-all strategy is crucial for effective AI governance.
• A tailored approach enhances the efficacy of international cooperation in managing AI's complexities.