The Original Sins of Algorithmic Authenticity Verification: Provenance Neglect and Model Collapse in AI-Mediated News and Information

This conceptual and prescriptive study identifies a foundational vulnerability in AI-mediated source and information verification systems, especially for journalism: provenance neglect, the systematic failure to weight evidence by source credibility, traceable origin, and institutional accountability. Grounded in source credibility theory, we demonstrate how this design flaw produces circular reasoning, synthetic content amplification, weakness that can be exploited by malicious actors, and even model collapse. In response, we propose Provenance-Weighted Authenticity Verification (PWAV), a sociotechnical framework integrating cryptographic content credentials, professional verification standards, corporate expectations, and regulatory alignment to support trustworthy AI-mediated verification for news and allied quests for knowledge. We discuss theoretical contributions, practical implications for newsrooms, and directions for future research.