Tzu-Sheng Kuo 郭子生

@tskuo.bsky.social

PhD student @ CMU HCII I build systems that empower people to shape AI through collaborative, deliberative, and democratic processes. https://tskuo.github.io

☀️ For the 2nd year in a row, I had the honor of serving as Student Volunteer Co-Chair, leading an incredible team of over 200 SVs at @chi.acm.org My heartfelt thanks go out to our SVs, the true MVPs who work tirelessly behind the scenes. We hope all attendees had a wonderful conference!

A group photo of 200 student volunteers standing beneath the huge CHI 26 sign.

CSCW folks, I wanted to highlight how excited and proud I am to see work from our community (dl.acm.org/doi/10.1145/..., CSCW '24 best paper winner led by @jiachenyan.bsky.social and @mlam.bsky.social) grow and expand ambition into this Science paper. CSCW has a ton to offer the world.

Michael Bernstein@mbernst.bsky.social · 8mo ago

Our new article in @science.org enables social media reranking outside of platforms' walled gardens. We add an LLM-powered reranking of highly polarizing political content into N=1256 participants' feeds. Downranking cools tensions with the opposite party—but upranking inflames them.

screenshot of the title and authors of the Science paper that are linked in the next post

Really excited about this 🔔new paper🔔 where we had a chance to leverage Change.org 's staggered rollout of a "write with AI" tool to causally (aka "once and for all") measure the impact of such tools on global platform outcomes. Summary: with AI, petition length ⬆️, homogeneity ⬆️, Outcomes ⬇️. More:

Isabel Silva Corpus@isabelcorpus.bsky.social · 8mo ago

Excited to share a new working paper! What happened when Change.org integrated an AI writing tool into their platform? We provide causal evidence that petition text changed significantly while outcomes did not improve. 1/ arxiv.org/abs/2511.13949

I'm excited to serve as an SV co-chair for #CHI2026! Interested in joining us? Apply now to become an SV! It's a fantastic opportunity to make new friends and help create an unforgettable @chi.acm.org experience. Check out the application link below:

Call for Enrollment
CHI 2026
Student Volunteer
Application Period
Oct 15 2025 - Jan 23 2026

LLM safety work often reasons over high-level policies (be helpful & polite), but must tackle on-the-ground cases (unsolicited money advice when stocks are mentioned). This can feel like driving on an unfamiliar road guided by a generic driver’s manual instead of a map. We introduce: Policy Maps 🗺️

🌟 If you’re applying to CMU SCS PhD programs, and come from a background that would bring additional dimensions to the CMU community, our PhD students are here to help! Apply to the Graduate Applicant Support Program by Oct 13 to receive feedback on your application materials:

Carnegie Mellon University School of Computer Science Graduate Application Support Program. Apply by October 13, 2025.

We are organizing a workshop on Algorithmic Collective Action at NeurIPS this year. As AI continues to concentrate power, we will meet in San Diego (Dec 6 or 7) for critical conversations on user coordination, labor, data protection, and community advocacy. Submissions due August 22. #NeurIPS2025

The image is titled Workshop on Algorithmic Collective Action. On the left are speakers, Tijana Zrnic, Incoming Assistant Professor, Stanford University, Seda Gürses, Associate Professor, TU Delft, lorian Tramèr, Assistant Professor, ETH Zürich, Alex Hanna, Director of Research, Distributed AI Research Institute (DAIR), Joanna Redden, Associate Professor, Western University, and Saiph Savage, Assistant Professor, Northeastern University. 

The abstract reads:

The study of “collective action” has a long history in Economics and Sociology as a way for groups of people to impact markets and the political arena (Olson 1965; Marwell and Oliver 1993). Algorithmic Collective Action (ACA) is the study of such coordination strategies in algorithmically-mediated sociotechnical systems. Our workshop offers a platform to discuss new ideas and help define the foundational research directions for the emerging topic through interdisciplinary discussions between ML researchers, scholars from the social sciences, community stakeholders and advocates.

Prompting is our most successful tool for exploring LLMs, but the term evokes eye-rolls and grimaces from scientists. Why? Because prompting as scientific inquiry has become conflated with prompt engineering. This is holding us back. 🧵and new paper with @ari-holtzman.bsky.social .

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Thrilled to share that I’ve successfully defended my PhD dissertation and I will be joining MIT as an Assistant Professor starting Fall 2026, with a shared appointment between Sloan and EECS! I will be recruiting 1-2 PhD students this upcoming cycle. Consider applying to MIT EECS!

Somewhat oddly, the Trump regime's initial moves to cut science funding went very broad. This is catalyzing solidarity & advocacy around the value of science (see comms featuring cancer cures & tech innovation). But their next move may be to try to drive a wedge b/w "good science" & "bad science".

Groups use processes to make decisions. But normal people with no training will usually run a bad process. When you just perform how you think meetings are supposed to work, without training, your meetings will be boring or bad. AND, big secret: when everyone's trained, meetings feel good.

I would love to never see the phrase "little is known about" in a paper ever again. (1) Most of the time the authors are far more confident about that statement than they should be. (2) There are lots of things we know little about because no one cares. Just not knowing isn't a good motivation.