AI Governance has been a hot topic this year, in Japan, China, the US, the EU, and many other polities.
For policymakers to make informed decisions about effective governance of AI, they need reliable and accurate information about AI capabilities, current limitations, and future trajectories. This means they need access to technical experts who can not only articulate the realistic landscape of current capabilities but to explain it in a way that is digestible and understandable for policymakers. In a recent position paper, Reuel and Soder et al. claim that governments lack this access, often creating regulations that cannot be realized without significant research breakthroughs.
We are one of the few AI safety groups with both technical and legal expertise. Let’s review the position paper, discuss whether its claims are merited, and ask how we might be able to get more technical people into government. Since the paper only looks at the US, the EU and China, and then bills introduced in 2023, it might also be fun to apply the same methodology to more recent legislation, and to Japan’s Basic Law for Promoting Responsible AI and AI Guidelines for Business.
In light of recent advancements in AI capabilities and the increasingly widespread integration of AI systems into society, governments worldwide are actively seeking to mitigate the potential harms and risks associated with these technologies through regulation and other governance tools. However, there exist significant gaps between governance aspirations and the current state of the technical tooling necessary for their realisation. In this position paper, we survey policy documents published by public-sector institutions in the EU, US, and China to highlight specific areas of disconnect between the technical requirements necessary for enacting proposed policy actions, and the current technical state of the art. Our analysis motivates a call for tighter integration of the AI/ML research community within AI governance in order to i) catalyse technical research aimed at bridging the gap between current and supposed technical underpinnings of regulatory action, as well as ii) increase the level of technical expertise within governing institutions so as to inform and guide effective governance of AI.