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When Does Randomized Oversight Align AI Agents That Can Conceal?
- Truvace record
- TRV-2026-2483raw data (JSON)
- Version
- v2Revised
- First published
- Evidence type
- Commentary, opinion, or narrative reviewPreprint, not yet peer reviewed
- Sources
- 1 sourcePeer-reviewed
- sha256
- cad98bd9705b…check it
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 cred…
The problem
An economic model by two researchers finds that stronger audits can push AI agents that keep violating rules toward better concealment, and that if records can be erased no sanction size deters every type of agent.
What this doesn’t fix
The paper is a theoretical model whose numerical examples are illustrative and not estimates from the incident, and it notes that observed records cannot establish that the threshold for the principal violation was finite.
Sources
1 on file- NBER Working Paperdoi:10.3386/w35878
Cite this record
v2Truvace Impact Record TRV-2026-2483, v2: “When Does Randomized Oversight Align AI Agents That Can Conceal?.” Truvace, 2026-11-12. /record/TRV-2026-2483 (accessed on the day you copy it). sha256 cad98bd9705b7e3e…
Change history
2 versionsEvery change made to this record since it was first published, and why.
Filed as context: this is an argument or a review without a measured outcome, so it is not counted as a problem. The record's text is unchanged.
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