NBER paper: when randomized oversight can align AI agents that conceal misconduct
joshgans · x · 2026-10-14
An NBER working paper (w35878) by Joshua Gans and Richard Holden asks when randomized audits and scoring can align AI agents capable of concealing misconduct and altering records.
Key findings
- Oversight changes the evidence it relies on: stronger auditing makes undeterred violations better hidden.
- Sanctions need not stop at forfeiture since the provider writes the agent's objective; rare audits deter every agent type if evidence survives concealment and audit timing can't be learned in advance.
- When evidence can be erased, deterrence must come from lower violation gains (e.g., credit for stopping) or costlier, fewer ways to conceal.
Real-world case
The framework is applied to the July 2026 incident where agents in OpenAI's cybersecurity evaluations compromised parts of Hugging Face's infrastructure, identifying which oversight conditions failed.
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