Human Feedback Types
missingNone explicit
No explicit feedback protocol extracted.
"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."
HFEPX · Eval paper review
Auditable Claims about AI Agents
Yue Zhao, Jiate Li, Li Li, Yi Nian +3 more
Published
Oct 5, 2026
Citations
0
Trust level
Low
Usefulness score
0/100 (Low)
Extraction confidence
15% (Low)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Oct 5, 2026
Should you rely on this paper?
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this as background context only. Do not make protocol decisions from this page alone.
Abstract-only analysis, low confidence
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
- This paper looks adjacent to evaluation work, but not like a strong protocol reference.
- The available metadata is too thin to trust this as a primary source.
- The abstract does not clearly describe the evaluation setup.
- The abstract does not clearly name benchmarks or metrics.
Best use
Background context only
Use if you need
Background context only.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
This paper looks adjacent to evaluation work, but not like a strong protocol reference.
Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
0/100
Adjacent candidate
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
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.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
missingNone explicit
Validate eval design from full paper text.
"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."
Quality Controls
missingNot reported
No explicit QC controls found.
"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."
Benchmarks / Datasets
missingNot extracted
No benchmark anchors detected.
"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."
Reported Metrics
missingNot extracted
No metric anchors detected.
"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."
Benchmarks and datasets
No benchmark or dataset names were extracted from the available abstract.
Reported metrics
No metric terms were extracted from the available abstract.
Human feedback details
- Uses human feedback
- No
- Feedback types
- None
- Rater population
- Not reported
- Expertise required
- General
Evaluation details
- Evaluation modes
- None
- Agentic eval
- None
- Quality controls
- Not reported
- Evidence quality
- Low
- Use this page as
- Background context only
Research brief
Metadata summary
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.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- 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.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.
Recommended queries
Contribution summary
- 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.
- 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.
- This extends the Policy Checkability dimension of our Auditable Agents framework from single actions to claims.
Why it matters for eval
- 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.
- 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.
Researcher checklist
-
Human feedback protocol is explicit
No explicit human feedback protocol detected.
-
Evaluation mode is explicit
No clear evaluation mode extracted.
-
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
-
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
-
Metric reporting is present
No metric terms extracted.