Peer review is facing a death spiral, and AI production tools are speeding it up. AI-assisted reviewing is necessary and should be open. We built OpenAIReview: open AI reviewing for everyone, for the cost of a coffee. openaireview.github.io/blog.html 🧵

Local ballot measures are now on CivicChats! Local elections happen year-round, 10+ states have measures coming up in the next few months. Check your ballot and think through what you'll be voting on → civicchats.org

Democracy depends on an informed electorate. But political issues and ballot measures can be confusing, obscuring the effects of one outcome versus another. And politics is personal. Once we make an initial decision, it can be hard to see things from "the other side."

It predicts pretty well—not just shifts in the last week, but also: 1. Who’s working an overnight shift (in our data + external validation in MIMIC) 2. Who’s working on a disruptive circadian schedule 3. How many patients has the doc seen *on the current shift*

🧑‍⚖️How well can LLMs summarize complex legal documents? And can we use LLMs to evaluate? Excited to be in Albuquerque presenting our paper this afternoon at @naaclmeeting 2025!

Although I cannot make #NAACL2025, @chicagohai.bsky.social will be there. Please say hi! @chachachen.bsky.social GPT ❌ x-rays (Friday 9-10:30) @mheddaya.bsky.social CaseSumm and LLM 🧑‍⚖️ (Thursday 2-3:30) @haokunliu.bsky.social @qiaoyu-rosa.bsky.social hypothesis generation 🔬 (Saturday at 4pm)

1/n You may know that large language models (LLMs) can be biased in their decision-making, but ever wondered how those biases are encoded internally and whether we can surgically remove them?

Spent a great day at Boulder meeting new students and old colleagues. I used to take this view every day. Here are the slides for my talk titled "Alignment Beyond Human Preferences: Use Human Goals to Guide AI towards Complementary AI": chenhaot.com/talks/alignm...

How do everyday narratives reveal hidden cause-and-effect patterns that shape our beliefs and behaviors? In our paper, we propose Causal Micro-Narratives to uncover narratives from real-world data. As a case study, we characterize the narratives about inflation in news.