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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., Comment: 9 pages, 3 figures, Proceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024

Because of the speed of its development, broad scope of application, and its ability to augment human performance, generative AI challenges the very notions of governance, trust, and human agency. The technology's capacity to mimic human knowledge work, feedback loops including significant uptick in users, research, investor, policy, and media attention, data and compute resources, all lead to rapidly increasing capabilities. For those reasons, adaptive governance, where AI governance and AI co-evolve, is essential for governing generative AI. In sharp contrast to traditional governance's regulatory regimes that are based on a mix of rigid one-and-done provisions for disclosure, registration and risk management, which in the case of AI carry the potential for regulatory misalignment, this paper argues that generative AI calls for adaptive governance. We define adaptive governance in the context of AI and outline an adaptive AI governance framework. We outline actors, roles, as well as both shared and actors-specific policy activities. We further provide examples of how the framework could be operationalized in practice. We then explain that the adaptive AI governance stance is not without its risks and limitations, such as insufficient oversight, insufficient depth, regulatory uncertainty, and regulatory capture, and provide potential approaches to fix these shortcomings.

AI-enabled synthetic biology has tremendous potential but also significantly increases biorisks and brings about a new set of dual use concerns. The picture is complicated given the vast innovations envisioned to emerge by combining emerging technologies, as AI-enabled synthetic biology potentially scales up bioengineering into industrial biomanufacturing. However, the literature review indicates that goals such as maintaining a reasonable scope for innovation, or more ambitiously to foster a huge bioeconomy don't necessarily contrast with biosafety, but need to go hand in hand. This paper presents a literature review of the issues and describes emerging frameworks for policy and practice that transverse the options of command-and control, stewardship, bottom-up, and laissez-faire governance. How to achieve early warning systems that enable prevention and mitigation of future AI-enabled biohazards from the lab, from deliberate misuse, or from the public realm, will constantly need to evolve, and adaptive, interactive approaches should emerge. Although biorisk is subject to an established governance regime, and scientists generally adhere to biosafety protocols, even experimental, but legitimate use by scientists could lead to unexpected developments. Recent advances in chatbots enabled by generative AI have revived fears that advanced biological insight can more easily get into the hands of malignant individuals or organizations. Given these sets of issues, society needs to rethink how AI-enabled synthetic biology should be governed. The suggested way to visualize the challenge at hand is whack-a-mole governance, although the emerging solutions are perhaps not so different either.