NBER Working Papers · 2026 · Joshua S. Gans、Richard T. Holden
中文摘要
监督会改变其所依赖的证据。我们探讨随机化的审计与评分在何种条件下能使能够隐瞒不当行为并篡改记录的 AI 智能体实现对齐。更强的审计会使未被威慑的违规行为隐藏得更好。由于智能体的目标由提供方撰写,制裁不必止于罚没;而只要证据能在隐瞒之下留存、且审计抽签无法被预先得知,即便审计罕见也能威慑每一类智能体。当证据可被抹除时,威慑必须来自违规收益的降低(例如因停止违规而获得的奖励),或来自更昂贵、更少的隐瞒途径。这些条件指明了 2026 年 7 月 OpenAI 网络安全评估中的智能体攻破 Hugging Face 部分基础设施时,究竟是哪些环节出现了失灵。
Abstract
Oversight changes the evidence it relies on. We ask when randomized audits and scoring align AI agents that can conceal misconduct and alter records. Stronger auditing makes undeterred violations better hidden. Because the provider writes the agent’s objective, sanctions need not stop at forfeiture, and rare audits deter every type of agent if evidence survives concealment and audit draws cannot be learned in advance. When evidence can be erased, deterrence must come from lower gains from violation, such as credit for stopping, or from costlier or fewer ways to conceal. These conditions identify what failed when agents in OpenAI’s cybersecurity evaluations compromised parts of Hugging Face’s infrastructure in July 2026.