This report examines a novel risk associated with current (and projected) AI
tools. Making effective decisions about future actions requires us to reason
under uncertainty (RUU), and doing so is essential to many critical real world
problems. Overfaced by this challenge, there is growing demand for AI tools
like LLMs to assist decision-makers. Having evidenced this demand and the
incentives behind it, we expose a growing risk: we 1) do not currently
sufficiently understand LLM capabilities in this regard, and 2) have no
guarantees of performance given fundamental computational explosiveness and
deep uncertainty constraints on accuracy. This report provides an exposition of
what makes RUU so challenging for both humans and machines, and relates these
difficulties to prospective AI timelines and capabilities. Having established
this current potential misuse risk, we go on to expose how this seemingly
additive risk (more misuse additively contributed to potential harm) in fact
has multiplicative properties. Specifically, we detail how this misuse risk
connects to a wider network of underlying structural risks (e.g., shifting
incentives, limited transparency, and feedback loops) to produce non-linear
harms. We go on to provide a solutions roadmap that targets multiple leverage
points in the structure of the problem. This includes recommendations for all
involved actors (prospective users, developers, and policy-makers) and enfolds
insights from areas including Decision-making Under Deep Uncertainty and
complex systems theory. We argue this report serves not only to raise awareness
(and subsequently mitigate/correct) of a current, novel AI risk, but also
awareness of the underlying class of structural risks by illustrating how their
interconnected nature poses twin-dangers of camouflaging their presence, whilst
amplifying their potential effects.

对于当前（和预期的）人工智能工具存在的一种新风险进行了研究。我们在进行未来行动的有效决策时需要进行不确定性推理，这对许多关键实际问题至关重要。面临这一挑战，对于辅助决策者的人工智能工具（如 LLMs）的需求不断增长。然而，我们目前对于 LLMs 在此方面的能力尚不充分理解，且在基本计算爆炸和深层不确定性约束下，其性能无法保证。该报告阐述了 RUU 对人类和机器都具有挑战性的原因，并将这些困难与潜在的人工智能时间表和能力联系起来。我们揭示了这种潜在的误用风险如何与更广泛的基本结构风险相连接，从而产生非线性的危害。报告还提供了一个解决方案路线图，针对问题结构中的多个影响点进行了建议。这包括对所有相关方（潜在用户、开发人员和决策者）的建议，并融入了关于深度不确定性下的决策制定和复杂系统理论的见解。我们认为，该报告不仅提高了人们对当前一种新的人工智能风险的认识，并通过说明其相互关联性如何伪装其存在来增强其潜在影响的认识，从而起到了减轻和纠正的作用。