Observability & Evaluation · Software component
Reasoning Faithfulness Tester
Software componentObservability & EvaluationObservability & Evaluationarc:ReasoningFaithfulnessTester
An evaluation component that tests whether a model's verbalised reasoning actually drives its answers, using symmetric or perturbed question probes and causal analysis of which steps influence the output.
Responsibility. Measures the faithfulness, rather than the fluency, of chain-of-thought explanations.
Also known as: CoT faithfulness evaluator, Causal step-importance analyzer, Explanation fidelity test, Explanation stability test
Relationships
reads dependency
evaluates assurance
Design guidance
- MUST NOT treat CoT output as guaranteed explainability; treat it as a hypothesis about how the model reasoned.
- SHOULD measure task accuracy independently of CoT coherence scores to detect metric gaming.
- SHOULD identify critical-path reasoning steps to focus validation and error correction.
- SHOULD verify that removing features cited by an explanation changes the prediction significantly (Ref10.02).
- SHOULD verify that slightly perturbed inputs receive similar explanations (Ref10.02).
Quantitative guidance
As stated by the sources; verify before use.
- Contradictory answers on symmetric comparison questions: GPT-4o-mini 13%, Claude Haiku 3.5 7%, Gemini 2.5 Flash 2.17%, ChatGPT-4o 0.49% (Ch5.1).
- About 25% of recent papers (38% medical AI, 25% AI-for-law, 63% autonomous-vehicle papers using CoT) wrongly treat CoT as interpretability (Ch5.1).
- Example thresholds: prediction change >0.3 when cited features removed; explanation similarity >0.9 under noise 0.01-0.1 (Ref10.02).
Classification
- Patterns
- Symmetric-question consistency probingAdversarial prompt perturbationCausal step-influence analysis
- Quality attributes
- Explainability (NIST AI RMF: explainable and interpretable)Interaction capability (ISO/IEC 25010)
- Risks mitigated
- Unfaithful post-hoc CoT rationalisationBias masked by plausible reasoningSilent error correction hiding erroneous stepsOptimising CoT coherence metrics instead of accuracy
Sources
- Ch5.1: T. Nguyen, "Chain-of-Thought (CoT) Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.1. ISBN: 9798244538229.
- Ch10.5: T. Nguyen, "Human-over-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.5. ISBN: 9798244538229.
- Ref10.02: "Explainability and Interpretability in Agent Systems," unpublished reference note (02-Explainability-Interpretability.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note