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Prediction certification cannot replace explanation certification: a competence envelope for trustworthy AI under compound stress

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TL;DR - This paper proves that prediction-based safeguards such as accuracy, calibration, and conformal coverage cannot by themselves certify trustworthy AI. It proposes a “competence envelope” that combines prediction and explanation certification to expose otherwise invisible failures.

  • A reliable model and a compromised model can satisfy identical prediction-side certificates while differing arbitrarily in explanation fidelity and deployment behavior.
  • Detecting this separation requires evidence about the model’s decision mechanism, not merely its outputs.
  • The competence envelope provides a deployable criterion integrating prediction performance with explanation fidelity.
  • Experiments across multiple datasets and model classes reveal failure modes missed by prediction-only certification.

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Prediction certification cannot replace explanation certification: a competence envelope for trustworthy AI under compound stress

arXiv cs.AI Nataliya Shakhovska, Ivan Izonin, Stergios-Aristoteles Mitoulis 2026-08-21 arXiv:2608.20825
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-10 14:23:51.503120 UTC

TL;DR - This paper proves that prediction-based safeguards such as accuracy, calibration, and conformal coverage cannot by themselves certify trustworthy AI. It proposes a “competence envelope” that combines prediction and explanation certification to expose otherwise invisible failures.

  • A reliable model and a compromised model can satisfy identical prediction-side certificates while differing arbitrarily in explanation fidelity and deployment behavior.
  • Detecting this separation requires evidence about the model’s decision mechanism, not merely its outputs.
  • The competence envelope provides a deployable criterion integrating prediction performance with explanation fidelity.
  • Experiments across multiple datasets and model classes reveal failure modes missed by prediction-only certification.
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