Banner image with screenshot of scientific article from nature Medicine, as well as two panels from the study method and results
⚠️ Despite all the hype, chatbots still make terrible doctors. Out today is the largest user study of language models for medical self-diagnosis. We found that chatbots provide inaccurate and inconsistent answers, and that people are better off using online searches or their own judgment.
Join us for a Data Discussion on December 12! 📅
Yani Kartalis will discuss content plurality in Greece’s fragmented media system, while Tereza Blažková will present on participatory design in predictive modeling for student success.
Event🔗: sodas.ku.dk/events/sodas...
Check out @isabelcorpus.bsky.social's fantastic thread on our paper studying the effects of a "write with AI" button on change.org! ✍️ Spoiler: the effects of AI aren't always positive.
Join us for a Data Discussion on Friday, November 7! 📅
Daniel Juhász Vigild will start by exploring how government use of AI impacts its trustworthiness, while Stephanie Brandl will examine whether LLMs can identify and classify fine-grained forms of populism.
Event🔗: sodas.ku.dk/events/sodas...
time to implement it into healthcare systems www.reddit.com/r/ChatGPT/co...
Cheers from Learning@Scale poster 033 😋
Loved working on this with @kizilcec.bsky.social, Magnus Lindgaard Nielsen, David Dreyer Lassen, and @andbjn.bsky.social , thank you for the collaboration, looking forward to future work!
To learn more, check out our paper and TikTok-style video, or see my poster and talk this week in Palermo!
Paper and video : dl.acm.org/doi/10.1145/...
Poster session: learningatscale.acm.org/las2025/inde...
Lightning talk: fair4aied.github.io/2025/
Depending on the time point and fairness metric, we observe both alarming disparities and confidence intervals that include zero.
While human behavior and the data describing it evolve over time, fairness is often evaluated at a single snapshot. Yet, as we show in our newly published paper, fairness is dynamic. We studied how fairness evolves in dropout prediction across enrollment and found that it shifts over time.
Happy to write this News & Views piece on the recent audit showing LLMs picking up "us versus them" biases: www.nature.com/articles/s43... (Read-only version: rdcu.be/d5ovo)
Check out the amazing (original) paper here: www.nature.com/articles/s43...
