We're happy to officially announce the location of #FAccT2026! Next year's conference will be held in Montreal, Canada 🇨🇦 Su Lin Blodgett and Zeerak Talat will be General Chairs, and Michael Madaio will be PC Chair 🎉 (thanks to MindView for the photo!)

Our #FAccT2025 proceedings are out!! 🎉 Read all of the published papers here: dl.acm.org/doi/proceedi...

Looking for posts about #FAccT2025? Check out our 🦋 custom feed 🦋 which is already lively and full of papers, events, and attendees for this year's conference in Athens! Click the pin 📌 in the upper right hand corner to keep this feed quickly accessible. bsky.app/profile/mari...

We are thrilled to welcome an incredible lineup of invited speakers to the 4th Explainable AI for Computer Vision (XAI4CV) Workshop, held as part of #CVPR2025 — which kicks off next week, from Wednesday, June 11th to Sunday, June 15th in Nashville, TN!

Commencement 🐯🎓🎉

📢 I successfully defended my PhD dissertation! Huge thanks to my committee (Olga @andresmh.com @jennwv.bsky.social @qveraliao.bsky.social @parastooabtahi.bsky.social) & everyone who supported me ❤️ 📢 Next I'll join Apple as a research scientist in the Responsible AI team led by @jeffreybigham.com!

Exciting news!!! This just got into @icmlconf.bsky.social as a position paper!!! 🎉 More updates to come as we work on the camera-ready version!!!

#CHI2025 = friends, friends, and friends (many not in the pics) 🥰 Research can be lonely, so moments like these are special! Thank you all for an amazing week of learning and fun. Hope to be back soon!!

I'll be at #CHI2025 🌸 to share 1 paper and 2 LBW on fostering appropriate user understanding and reliance on AI (LLMs and not). Let's catch up and connect! What I've been up to: job market & research (responsible AI, human-centered evaluation, overreliance, anthropomorphism) 🤓

Check out Indu Panigrahi’s LBW at #CHI2025: “Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations.” 🔗 Project Page: ind1010.github.io/interactive_XAI 📄 Extended Abstract: arxiv.org/abs/2504.10745

"Fostering Appropriate Reliance on LLMs" received an Honorable Mention at #CHI2025! This work is also the last chapter of my dissertation, so the recognition feels more special🏅🎓😊 🎉 to the team @jennwv.bsky.social @qveraliao.bsky.social @tanialombrozo.bsky.social Olga Russakovsky

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.

NEW from my team: a framework that walks AI product teams step-by-step through understanding and mitigating the risk of overreliance on AI. This happens when ppl accept incorrect AI outputs, b/c we … learn.microsoft.com/en-us/ai/pla...

Trying something new: A 🧵 on a topic I find many students struggle with: "why do their 📊 look more professional than my 📊?" It's *lots* of tiny decisions that aren't the defaults in many libraries, so let's break down 1 simple graph by @jburnmurdoch.bsky.social 🔗 www.ft.com/content/73a1...

New research on the role of explanations, sources, and inconsistencies in fostering appropriate reliance, led by the always amazing @sunniesuhyoung.bsky.social! 👇👇👇

Another very cool work led by the very cool @sunniesuhyoung.bsky.social, coming out at #CHI2025. Check it out 👇

Already a time capsule, but back in January a bunch of us working at the intersection of the humanities and AI/ML came together to sketch out eight provocations from the humanities for genAI research. Here's a 🧵 1/ arxiv.org/abs/2502.19190

Super fun collaboration across cogsci fields! We find that when an LLM response is accompanied by an explanation, users are more likely to accept the response. But the quality of the explanation matters - users rely less on responses with explanations when the explanations contain inconsistencies.

Submit your latest work (papers, demos) in #XAI to the 4th Explainable AI for Computer Vision (XAI4CV) Workshop at #CVPR2025! Details: xai4cv.github.io/workshop_cvp... Submission Site: cmt3.research.microsoft.com/XAI4CV2025 @cvprconference.bsky.social @xai-research.bsky.social

Appropriate reliance is key to safe and successful user interactions with LLMs. But what shapes user reliance on LLMs, and how can we foster appropriate reliance? In our #CHI2025 paper, we explore these questions through two user studies. 1/7