Examining AI Safety as a Global Public Good:
Implications, Challenges, and Research Priorities

Drawing on lessons from climate change, nuclear safety, and global health governance, this analysis examines whether and how applying the framework of a "public good" could help us better understand and address the challenges posed by advanced AI systems.

Tom David, CEO of the GPAI Policy Lab, along with 14 other scholars from around the world, co-authored this report.

Abstract

As artificial intelligence (AI) systems become more powerful and integrated into daily life and global infrastructure, ensuring their safe development and deployment has emerged as one of the most pressing governance challenges of our time. While current narrow AI systems already have significant impacts in specific domains, advanced AI systems could fundamentally transform life through their potential for recursive self-improvement and general problem-solving capabilities, making safety considerations increasingly critical.

This paper analyzes global public good literature frameworks and emerging AI governance challenges. Our analysis reveals several key challenges for overcoming coordination problems:

Balancing Collective Responsibility with Targeted Accountability: AI safety requires broad cooperation, but this must not diminish the accountability of leading AI developers and states that possess disproportionate power and leverage to ensure safe development. However, the stark disparity between nations developing frontier AI versus nations primarily implementing AI created elsewhere has fomented complex dynamics for international cooperation, such as by constraining global coordination efforts.

Safety-Capability Entanglement: Pursuing some critical AI safety measures may also involve advancing capabilities; alternatively, some critical AI safety measures may even require advanced capabilities to implement. These aspects create tension between the goals of sharing safety advances (on the one hand) and limiting the spread of AI capabilities with risky security or military implications (on the other).

Development Equity: It is important to ensure that AI safety requirements do not unduly constrain AI's involvement in strategies to achieve global and sustainable development goals like poverty reduction and also do not perpetuate long-standing inequities in the global system.

Rather than advocating for specific policy measures, this analysis advances our understanding of how collective action mechanisms might effectively address AI safety challenges while promoting equity and maintaining clear lines of responsibility. In addition, we offer avenues for further research in studying the application of the global public goods framework to AI safety.