X brings AI into Community Notes to fight misinformation at scale humans can't match

Yahoo

a day ago

X, formerly known as Twitter, has started testing a new approach in its Community Notes program by bringing AI into the mix. The company has begun piloting the use of large language models (LLMs) to write contextual notes that flag or clarify potentially misleading posts.
Community Notes began in 2021 as a user-driven initiative to combat misinformation. Volunteers could add notes under posts that might be deceptive or misinterpreted, such as labeling AI-generated videos to prevent people from believing false events. Human raters would then decide which notes were helpful. Only the most helpful notes would appear publicly.
The latest update adds AI-generated notes to this process, but with a key safeguard: only humans can rate which notes are ultimately shown.
'Allowing automated note creation would enable the system to operate at a scale and speed that is impossible for human writers,' the researchers write. They believe LLMs could 'potentially provide context for orders of magnitude more content across the web.'
The AI model will continue to evolve through a method called reinforcement learning from community feedback (RLCF).
The process refines note generation by using responses from people with a wide range of perspectives. The result, researchers say, should be notes that are more 'accurate, unbiased and helpful.'
Despite the promise of scale, researchers highlight several risks. AI-generated notes can sound persuasive even when they're wrong, a known issue with many LLMs. There's also a risk of producing too-similar notes, which could dilute the diversity of perspectives the system aims to include.
Another concern is that human contributors may participate less if AI generates too many notes. Human raters could also become overwhelmed trying to assess the volume of AI submissions.
The study lays out several potential solutions. One idea is to develop AI co-pilots that assist human writers with research, speeding up their output. Another is to use AI tools to help raters evaluate notes more efficiently.
To preserve quality, researchers also suggest vetting human contributors more thoroughly and customizing LLMs for note-writing. They propose adapting validated notes for reuse in similar cases, saving time and reducing redundancy.
Despite increasing automation, researchers stress the continued importance of human oversight. 'The goal is not to create an AI assistant that tells users what to think,' the study states, 'but to build an ecosystem that empowers humans to think more critically and understand the world better.'
X's move comes as misinformation continues to spread rapidly across social media platforms, often outpacing efforts to correct it. By combining human insight with AI's processing power, the company hopes to better manage false narratives in real time. The pilot will help determine whether this hybrid approach can maintain trust while boosting coverage.
The study is available on arXiv.