Auditing the Audit: Five Failure Modes in Benchmark-Validity Audits
Not on the map. Why?
Abstract (arXiv)
Governance frameworks ask AI providers and auditors for documented evaluation evidence, and perturbation-based construct-validity audits are a common form of that evidence. We argue the audits are themselves fragile: their conclusions can be silently manufactured by implementation details that readers cannot see in the reported numbers. We name five classes of pipeline failure and demonstrate each in a self-audit over safety benchmarks and open-weight instruction-tuned models. Under a unified six-point due-diligence gate, every cell lands in a non-confirmatory bucket, and no cell reaches confirmatory. The evidence here is a single two-model, five-benchmark case study, and F1--F5 is an illustrative, deliberately non-exhaustive starting taxonomy -- not a comprehensive partition of audit failures. We position the gate as a withholding and disclosure protocol for assurance-grade evidence, supplementary to (not a replacement for) classical construct-validity evidence, and not as a route to benchmark-validity verdicts.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 55 references that are in the map. First posted on arXiv 1 Jul 2026.
Builds on
25 of 55 references.
References · 25
- 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
- 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 1,006
- 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 817
- 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 710
- 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 701
- 2021Dynabench: Rethinking Benchmarking in NLPcited by 459
- 2022ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detectioncited by 422
- 2021AI and the Everything in the Whole Wide World Benchmarkcited by 349
- 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 310
- 2019Measurement and Fairnesscited by 308
- 2024When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboardscited by 137
- 2025AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agentscited by 90
13 more
- 2019Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowdscited by 40
- 2024FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Modelscited by 22
- 2026Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflictcited by 15
- 2026SafetyRepro: Configuration-Conditional Rank Instability on Alignment Benchmarkscited by 15
- 2026Relevant Is Not Warranted: Evidence-Force Calibration for Cited RAGcited by 14
- 2025CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Codingcited by 12
- 2025Attention Consistency for LLMs Explanationcited by 11
- 2026Retrieval--Reasoning Processes for Multi-hop Question Answering: A Four-Axis Design Framework and Empirical Trendscited by 8
- 2026SOK: A Taxonomy of Attack Vectors and Defense Strategies for Agentic Supply Chain Runtimecited by 8
- 2026AtelierEval: Agentic Evaluation of Humans & LLMs as Text-to-Image Prompterscited by 8
- 2026Pre-Registering the Detectable Effect: A Paired-MDE Budget for 4-bit Quantization Benchmarks, with a Pilot Auditcited by 8
- 2026Auditing Reasoning-Trace Memorization Claims after Unlearning with Head-Conditioned Canariescited by 7
- 2026Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choicecited by 6
Built on by
Citing papers per year: 2026: 1.
1 paper.