Tzu-Sheng Kuo 郭子生

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

479 followers246 following46 posts

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How can we help communities collaboratively shape policies that impact them? In our #CHI2025 paper, we present PolicyCraft, a system that supports ✨collaborative policy design✨ through case-grounded deliberation. (🧵/11)

☀️ 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!

Tzu-Sheng Kuo 郭子生 reposted

🔮 How can we empower online communities to design AI agents tailored to their unique needs and norms? In our #CHI2026 paper, we introduce #Botender, a system that enables collaborative design of AI agents through 🔥case-based provocation🔥

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12/ During a field deployment across six Discord communities, Botender supported community members in tailoring bot behavior to their specific needs, showcasing the usefulness of case-based provocations in facilitating collaborative bot design.

🔮 How can we empower online communities to design AI agents tailored to their unique needs and norms? In our #CHI2026 paper, we introduce #Botender, a system that enables collaborative design of AI agents through 🔥case-based provocation🔥

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10/ Botender presents case-based provocations to community members throughout the bot design process. This helps them collaboratively iterate on the bot’s prompts, review its behavior in specific cases based on these changes, and decide whether to deploy the proposed change.

🔮 How can we empower online communities to design AI agents tailored to their unique needs and norms? In our #CHI2026 paper, we introduce #Botender, a system that enables collaborative design of AI agents through 🔥case-based provocation🔥

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8/ To support this collaborative design process, we introduce 🔥case-based provocations🔥: Concrete interaction scenarios that are algorithmically generated to spark collective reflection and discussion about desirable bot behavior, rather than simply validating expected outcomes.

9/ Botender generates these provocations to surface opportunities for prompt improvement, based on common issues in novice-authored prompts. In turn, these cases are also useful in surfacing differing viewpoints about desirable bot behavior within a community.

🔮 How can we empower online communities to design AI agents tailored to their unique needs and norms? In our #CHI2026 paper, we introduce #Botender, a system that enables collaborative design of AI agents through 🔥case-based provocation🔥

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7/ To address these challenges, we present Botender, a system that supports communities in collaboratively designing LLM-powered bots that reflect their specific needs. With Botender, community members can directly propose, iterate on, and deploy custom bot behaviors on their community platform.

🔮 How can we empower online communities to design AI agents tailored to their unique needs and norms? In our #CHI2026 paper, we introduce #Botender, a system that enables collaborative design of AI agents through 🔥case-based provocation🔥

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4/ Recently, LLMs have lowered the technical barriers to designing AI agents, allowing communities to shape the behavior of LLM-powered bots using natural language, without relying on external developers. However, several challenges exist:

🔮 How can we empower online communities to design AI agents tailored to their unique needs and norms? In our #CHI2026 paper, we introduce #Botender, a system that enables collaborative design of AI agents through 🔥case-based provocation🔥

2/ AI agents, or bots, play diverse and important roles in community governance across online platforms. However, many communities rely on third-party bots developed by outsiders that misalign with their specific needs.

3/ This misalignment occurs because third-party bots typically offer limited customization options. Meanwhile, developers lack the community-specific knowledge and capacity to address tailored requests over time.

Tzu-Sheng Kuo 郭子生 reposted

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

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.

Tzu-Sheng Kuo 郭子生 reposted

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

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

Tzu-Sheng Kuo 郭子生 reposted

X turned on account location, and immediately turned it off again--because it showed that your "farmer in Michigan" is really a paid troll in Bangladesh. We need a social network that shows where content is from. asbruckman.medium.com/x-added-acco... With cites to @katestarbird.bsky.social

asbruckman.medium.comX added account location — and turned it off hours later.A window onto what is most broken about the internet.

Tzu-Sheng Kuo 郭子生 reposted

Just arrived in Bergen for CSCW, where I'll present Venire! Venire is a Reddit moderation tool that uses an ML model trained on mod decision histories to identify controversial cases. It preempts inconsistent decision-making by flagging these cases for multi-mod review dl.acm.org/doi/10.1145/...

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:

Tzu-Sheng Kuo 郭子生 reposted

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:

Tzu-Sheng Kuo 郭子生 reposted

Tzu-Sheng Kuo 郭子生 reposted

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

Tzu-Sheng Kuo 郭子生 reposted

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!

Tzu-Sheng Kuo 郭子生 reposted

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".

Tzu-Sheng Kuo 郭子生 reposted

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.

Serving as the Student Volunteer Co-Chair at #CHI2025 @chi.acm.org has been an incredible experience! A huge thank you to the 200 student volunteers who worked tirelessly behind the scenes to help make the conference a success. Looking forward to seeing you all next year in Barcelona!

Tzu-Sheng Kuo 郭子生 reposted

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.

AI research often aims to generate Wikipedia-like content, but Wikipedia’s success lies in the underlying processes and infrastructures that enable human collaboration. Rather than just mimicking content, we should use AI to support human collaboration. Our Wikibench paper is one such example:

doi.orgWikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia | Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems
Wikimedia Foundation@wikimediafoundation.org

While AI's capabilities are growing rapidly, people remain the trustworthy creators of the world's knowledge because they can do tasks – fact-check, reference text that exists offline – that AI cannot. Learn why human-led knowledge creation retains value ➡️ wikimediafoundation.org/news/2023/07...

Tzu-Sheng Kuo 郭子生 reposted

"When a complex system is far from equilibrium, small islands of coherence in a sea of chaos have the capacity to shift the entire system to a higher order." Iya Prigogine, Noble prize-winning chemist

Tzu-Sheng Kuo 郭子生 reposted

Check out our latest work on steerable alignment! We improve on a typical in-context learning approach with semantic retrieval by proposing metrics that instead select examples that best exemplify a group's shared values, enabling inference-time alignment to a particular group. ➡️

Quan Ze Chen@cqz.name

In-context learning can be an effective way to conduct value alignment of LLMs through examples, but when there are multiple pluralistic groups, are the best examples for one group also the ones for another? We explore this in our paper 🌟SPICA🌟 (🧵1/9)

Tzu-Sheng Kuo 郭子生 reposted

In-context learning can be an effective way to conduct value alignment of LLMs through examples, but when there are multiple pluralistic groups, are the best examples for one group also the ones for another? We explore this in our paper 🌟SPICA🌟 (🧵1/9)