In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?
Abstract
International cooperation is common in AI research, including between geopolitical rivals. While many experts advocate for greater international cooperation on AI safety to address shared global risks, some view cooperation on AI with suspicion, arguing that it can pose unacceptable risks to national security. However, the extent to which cooperation on AI safety poses such risks, as well as provides benefits, depends on the specific area of cooperation. In this paper, we consider technical factors that impact the risks of international cooperation on AI safety research, focusing on the degree to which such cooperation can advance dangerous capabilities, result in the sharing of sensitive information, or provide opportunities for harm. We begin by why nations historically cooperate on strategic technologies and analyse current US-China cooperation in AI as a case study. We further argue that existing frameworks for managing associated risks can be supplemented with consideration of key risks specific to cooperation on technical AI safety research. Through our analysis, we find that research into AI verification mechanisms and shared protocols may be suitable areas for such cooperation. Through this analysis we aim to help researchers and governments identify and mitigate the risks of international cooperation on AI safety research, so that the benefits of cooperation can be fully realised.
Cite
@article{arxiv.2504.12914,
title = {In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?},
author = {Ben Bucknall and Saad Siddiqui and Lara Thurnherr and Conor McGurk and Ben Harack and Anka Reuel and Patricia Paskov and Casey Mahoney and Sören Mindermann and Scott Singer and Vinay Hiremath and Charbel-Raphaël Segerie and Oscar Delaney and Alessandro Abate and Fazl Barez and Michael K. Cohen and Philip Torr and Ferenc Huszár and Anisoara Calinescu and Gabriel Davis Jones and Yoshua Bengio and Robert Trager},
journal= {arXiv preprint arXiv:2504.12914},
year = {2025}
}
Comments
Accepted to ACM Conference on Fairness, Accountability, and Transparency (FAccT 2025)