Auditable Claims about AI Agents
Source note: Preprints on societal impact.
arXiv:2610.07459v1 Announce Type: cross Abstract: Organizations make claims about their AI agents: a person approves every external email, every action is logged, an evaluation shows the agent is safe to deploy. Article 12 of the EU AI Act requires high-risk systems to allow the automatic recording of events but does not say which records settle a given claim. The position is one sentence: to be checked, a claim about an agent must first name its policy, its scope, the records that would settle it, and who writes them. Adapting the preconditions of an assurance engagement, we call a claim auditable when these elements and a decision rule are fixed before any verdict and the records are obtainable. This extends the Policy Checkability dimension of our Auditable Agents framework from single actions to claims. Agents add three conditions: coverage by an independent record, authorization bound to each action's arguments, and completeness beyond integrity. Under an explicit model, we prove that support is impossible without each wherever its hypotheses hold. A claim-check table applies the method to six common claims, anchored in current NIST, IETF, and OWASP drafts. A worked case follows one claim through five evidence states. We close with a practice box and steps for operators, buyers, auditors, and standard setters.
Every model that read this
| Model | Provider | Stage | Score | Conf. | Latency | Prompt | When |
|---|---|---|---|---|---|---|---|
| Llama 3.3 70B | Meta | analysis | 0 | 80% | 5092ms | v1.0.0 / m1.0.1 | 2026-10-07 05:41 |
The text discusses a framework for auditable claims about AI agents, but does not provide concrete evidence of its impact on society.
Evidence extracted
- Organizations make claims about their AI agents
- Article 12 of the EU AI Act requires high-risk systems to allow the automatic recording of events
SOURCE arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT c1324840-f1a3-4fc0-8cb9-af411945f239
https://arxiv.org/abs/2610.07459
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EVIDENCE 2 extracted excerpts
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MODEL RUN 1 run, methodology 1.0.1
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SCORE 0 (Mixed or uncertain)
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CONFIDENCE 80%