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February 20, 2026

Validating Generative AI-Based Social Sciences

Validating Generative AI-Based Social Sciences

Friday, Feb. 20
9 a.m. - 5 p.m.

Frances Searle Building, Center for Human-Computer Interaction + Design (Room 1-122), 2240 Campus Drive

Generative AI is reshaping how we study human behavior, but are we validating what we simulate? This event brings together researchers from social computing, design, and computational social science to tackle the questions at the heart of generative social science. As large language models (LLMs) increasingly generate social and behavioral data, we’ll explore what kinds of tests, methods, and systems are needed to ensure these simulations are credible, replicable, and truly useful.

With preprints and products emerging faster than the research community can connect, this event offers a timely space for interdisciplinary dialogue. This event will spark collaboration across disconnected teams and help shape the future of ethical, rigorous generative AI research.

Organizers

Guest Speakers

Serina Chang headshot

Serina Chang

Assistant Professor in Electrical Engineering and Computer Sciences and Computational Precision Health, UC Berkeley
View Serina Chang's Profile

Schedule

Time Activity Speakers
9 - 9:30 a.m. Opening / Welcome
  • Christopher Schuh, Dean of McCormick School of Engineering
  • Darren Gergle, Co-Director of the Center for Human-Computer Interaction + Design
  • Jessica Hullman, Ginni Rometty Professor of Computer Science and Faculty Fellow at the Institute for Policy Research at Northwestern University
9:30 - 10:30 a.m.

Lightning Talks and Q&A

  • The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations - David Broska
  • Why Human Interaction Matters for AI Evaluation—and How User Simulators Can Help - Serena Chang
  • Valid Survey Simulations with Limited Human DataKristina Gligoric
10:30 - 10:45 a.m. Break
10:45 a.m. - 12:15 p.m.

Lightning Talks and Q&A

  • Large language models that replace human participants can harmfully misportray and flatten identity groups - Angelina Wang
  • When Can We Trust Experiments on Digital Twins?  A Potential Outcomes Framework for Causal Inference with LLM Simulations - Patryk Perkowski
  • Causal Inference in Experiments with Mixed-Subjects Designs - Austin van Loon
12:15 - 1:30 p.m. Lunch 
1:30 - 3 p.m. Lightning Talks and Q&A
  • Semantic Structure in LLM Internal Representations - Austin Kozlowski
  • Detailed Self-Reports Can Reduce Bias in LLM-Simulated Survey RespondentsJonne Kramphorst
  • Synthetic Consumers in Practice: Methods, Validation, and Applied ChallengesMichael Spadafore
  • Social Science and the Bitter Lesson: Thoughts on Valid Social Science when Machines Do ResearchJohn Horton
3 - 3:30 p.m. Group Discussion and
Breakout Topic Refinement
3:30 - 4:30 p.m. Breakout Group Discussions
4:30 - 5 p.m. Report-Out and Wrap Up
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