AI 中文总结
该研究针对AI难以预测的问题，提出三部分组成的基于能力的规划方法框架，经四类AI赋能威胁试点验证，为AI危机准备提供实用工具。
AI 中文摘要
基于能力的规划推动了国防和国土安全领域的准备工作，但尚未被严肃应用于人工智能领域。政府的人工智能准备遵循“先预测后行动”的范式：按可能性和影响对风险进行排序，然后为预期危害最大的风险做准备。人工智能难以预测：专家的时间线存在数量级的分歧，官方审查也承认，基于可能性的风险评估对这类风险完全失效。基于深度不确定性下的决策原则，我们提出了一个由三部分组成的方法框架：一是在已声明的轴上系统采样的情景库；二是针对每个政府能力，基于粗粒度的门控标准对每个情景进行评估的评级程序；三是将所得矩阵映射到政府可能采用的决策规则的优先级排序步骤。通过对四种最严重的人工智能赋能威胁类别进行试点，我们展示了该工具能产生的洞见，并证明了基于能力的规划作为人工智能危机准备实用工具的概念验证。
英文摘要
Capability-based planning drives preparedness in defense and homeland security, but has yet to be applied seriously to AI. Government AI preparations follow a predict-then-act paradigm: rank risks by likelihood and impact, then prepare for the highest expected harm. AI resists prediction: expert timelines disagree by orders of magnitude, and official reviews concede that likelihood-based risk assessment fails for exactly this class of risk. Drawing on principles of decision making under deep uncertainty, we propose a methodological framework in three parts: a scenario library sampled systematically across declared axes; a rating procedure that assesses each government capability against each scenario on coarse, gated criteria; and a prioritization step that maps the resulting matrix onto decision rules a government might adopt. Through a pilot across the four most severe AI-enabled threat classes, we illustrate the kind of insight the instrument yields and provide a proof of concept for capability-based planning as a practical tool for AI crisis preparedness.
