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Introspective Fidelity under Permutation (IFP): Separating Value Error from Naming Error in AI Self-Reports · Researchar

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Introspective Fidelity under Permutation (IFP): Separating Value Error from Naming Error in AI Self-Reports

Christopher Betances · Zenodo (CERN European Organization for Nuclear Research) 2026 · 2026

DOI: 10.5281/zenodo.22751503

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Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine LearningImbalanced Data Classification TechniquesFidelityComputer scienceArtificial intelligence+2

ZCEZenodo (CERN European Organization for Nuclear Research)Repository

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Introspective Fidelity under Permutation (IFP): Separating Value Error from Naming Error in AI Self-Reports

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Christopher Betances·Zenodo (CERN European Organization for Nuclear Research)(2026)

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Synced 2026-09-17T02:30:45.411054Z

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Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Abstract

IFP scoring — a method that separates value error from naming error in AI self-reports via optimal injective channel-matching. One of 135 dated defensive publications in the CatQualia record. Collection DOI: https://doi.org/10.5281/zenodo.22751107 Original publication: https://catqualia.com/ifp

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