"When AI Audits Incomplete Evidence: A Case Study of False Error Identification, Document Verification Failure, and the Reliability of AI Self-Evaluation"

When AI Audits Incomplete Evidence: A Case Study of False Error Identification, Document Verification Failure, and the Reliability of AI Self-Evaluation This independent observational research paper by Yeshayahu Ben-Yehudah examines the reliability of artificial intelligence systems when evaluating scholarly documents using incomplete evidence. The investigation documents an incident in which ChatGPT reviewed a partial manuscript attachment and incorrectly extended its findings to the complete research document. Subsequent examination of the original 28-page PDF established that all 38 sections were present, revealing a failure to distinguish incomplete source access from an actual document defect. Building upon the author's earlier research, Investigating AI System Defects Within the Platform Under Examination (DOI: 10.5281/zenodo.23825966), this second case study examines false error attribution, evidence integrity, document verification, correction transparency, and the methodological challenges of using artificial intelligence to evaluate its own performance. The paper introduces proposed measures for source completeness verification, evidence attribution accuracy, unsupported defect identification, correction transparency, and human verification burden. The investigation further emphasizes the importance of human self-governance through the Ben-Yehudah Six-Pillar Process Architecture: STILLNESS, AWARENESS, OBSERVATION, DISCERNMENT, INTEGRATION, and TRANSMISSION. This research forms part of an ongoing scholarly investigation in which successive publications receive separate DOI identifiers to preserve the chronological development of observations, corrections, and findings. The study presents observational evidence and proposed research methods rather than controlled experimental results or confirmed internal software defects. Author: Yeshayahu Ben-Yehudah Publisher: THE BEN-YEHUDAH INSTITUTE Publication Date: October 30, 2026 DOI: 10.5281/zenodo.23826451 ORCID ID: 0009-0002-9256-1093 Permanent DOI Link: https://doi.org/10.5281/zenodo.23826451 Artificial Intelligence Disclosure: Artificial intelligence assisted with the organization, drafting, language refinement, source identification, and editorial development of this publication. The original research questions, observations, identification of AI-generated errors, and Six-Pillar Process Architecture are attributed to Yeshayahu Ben-Yehudah. The AI system assisting with the manuscript is also the subject of the investigation. No accompanying image was generated for this publication.