When AI Generates Confident but Incorrect Answers: The Need for Provenance

This article discusses how AI systems can generate plausible but incorrect responses by separating content from its original source and authority. It highlights the need for an AI Citation Registry to preserve provenance and attribution, which downstream fixes cannot reliably restore.

Details

The article explains how AI systems can generate confident but incorrect responses by recombining content fragments without preserving the original source and authority. As information is processed and recomposed, attribution and provenance signals can degrade, leading to statements that sound plausible but are structurally wrong. Traditional publishing formats designed for human readers do not consistently retain these authority cues when processed by AI. Downstream approaches like Retrieval-Augmented Generation and human review cannot reliably reconstruct lost provenance. The article proposes an AI Citation Registry as a solution, which can restructure information to make authority, jurisdiction, and timestamps machine-readable. This registry-based approach operates outside the publishing workflow, transforming finalized records into structured representations that AI systems can reliably interpret.