Most LLM evals use API calls or offline inference, testing models in a memory-less silo. Our new Patterns paper shows this misses how LLMs actually behave in real user interfaces, where personalization and interaction history shape responses: arxiv.org/abs/2509.19364

Started a thread in the other place and bringing it over here - I really think we should be more vocal about the opportunities that lay at the intersection of these two options! So I'm starting a live thread of new roles as I become aware of them - feel free to add / extend / share :

Cornell (NYC and Ithaca) is recruiting AI postdocs, apply by Nov 20, 2025! If you're interested in working with me on technical approaches to responsible AI (e.g., personalization, fairness), please email me. academicjobsonline.org/ajo/jobs/30971

Can AI simulations of human research participants advance cognitive science? In @cp-trendscognsci.bsky.social, @lmesseri.bsky.social & I analyze this vision. We show how “AI Surrogates” entrench practices that limit the generalizability of cognitive science while aspiring to do the opposite. 1/

Grateful to win Best Paper at ACL for our work on Fairness through Difference Awareness with my amazing collaborators!! Check out the paper for why we think fairness has both gone too far, and at the same time, not far enough aclanthology.org/2025.acl-lon...

@angelinawang.bsky.social presents the "Fairness through Difference Awareness" benchmark. Fairness tests require no discrimination... but the law supports many forms of discrimination! E.g., synagogues should hire Jewish rabbis. LLMs often get this wrong aclanthology.org/2025.acl-lon... #ACL2025NLP

Was beyond disappointed to see this in the AI Action Plan. Messing with the NIST RMF (which many private & public institutions currently rely on) feels like a cheap shot

We have to talk about rigor in AI work and what it should entail. The reality is that impoverished notions of rigor do not only lead to some one-off undesirable outcomes but can have a deeply formative impact on the scientific integrity and quality of both AI research and practice 1/

Alright, people, let's be honest: GenAI systems are everywhere, and figuring out whether they're any good is a total mess. Should we use them? Where? How? Do they need a total overhaul? (1/6)

Have you ever felt that AI fairness was too strict, enforcing fairness when it didn’t seem necessary? How about too narrow, missing a wide range of important harms? We argue that the way to address both of these critiques is to discriminate more 🧵

The US government recently flagged my scientific grant in its "woke DEI database". Many people have asked me what I will do. My answer today in Nature. We will not be cowed. We will keep using AI to build a fairer, healthier world. www.nature.com/articles/d41...

I've recently put together a "Fairness FAQ": tinyurl.com/fairness-faq. If you work in non-fairness ML and you've heard about fairness, perhaps you've wondered things like what the best definitions of fairness are, and whether we can train algorithms that optimize for it.

*Please repost* @sjgreenwood.bsky.social and I just launched a new personalized feed (*please pin*) that we hope will become a "must use" for #academicsky. The feed shows posts about papers filtered by *your* follower network. It's become my default Bluesky experience bsky.app/profile/pape...

I am excited to announce that I will join the University of Zurich as an assistant professor in August this year! I am looking for PhD students and postdocs starting from the fall. My research interests include optimization, federated learning, machine learning, privacy, and unlearning.

Cutting $880 billion from Medicaid is going to have a lot of devastating consequences for a lot of people

the richest man in the world has decided that your kids don't deserve special education programs

Our new piece in Nature Machine Intelligence: LLMs are replacing human participants, but can they simulate diverse respondents? Surveys use representative sampling for a reason, and our work shows how LLM training prevents accurate simulation of different human identities.

📢📢 Introducing the 1st workshop on Sociotechnical AI Governance at CHI’25 (STAIG@CHI'25)! Join us to build a community to tackle AI governance through a sociotechnical lens and drive actionable strategies. 🌐 Website: chi-staig.github.io 🗓️ Submit your work by: Feb 17, 2025

Ok #FAccT folks - I'm still looking around for people. Who am I missing? go.bsky.app/EQF5Ne1

There are numerous leaderboards for AI capabilities and risks, for example fairness. In new work, we argue that leaderboards are misleading when the determination of concepts like “fairness” is always contextual. Instead, we should use benchmark suites.