Overview
The 2nd Workshop on AI Behavioral Science (AIBS 2026) is the second annual event bringing together leading researchers and practitioners at the intersection of artificial intelligence and behavioral science. Building on the successful inaugural workshop in 2025, which explored the foundational aspects of AI Behavioral Science, the 2026 workshop will focus specifically on human-AI interaction. This event will feature discussions across three core themes:
- Human Perception and Mental Models of AI
- AI as Decision Support and Behavioral Intervention
- Strategic Interaction Between Humans and AI
This workshop aims to catalyze discussions and collaborations that will shape the future of this promising field by bringing together thought leaders and practitioners from across disciplines.
Program
To be announced.
Organization
Please contact us through this email address if you have any questions.
Matthew O. Jackson
Stanford University
https://web.stanford.edu/~jacksonm/
Bio: Matthew O. Jackson is the William D. Eberle Professor of Economics at Stanford University and an external faculty member of the Santa Fe Institute. He was at Northwestern University and Caltech before joining Stanford, and received his BA from Princeton University in 1984 and PhD from Stanford in 1988. Jackson’s research interests include game theory, microeconomic theory, and the study of social and economic networks, on which he has published many articles and the books ‘The Human Network’ and ‘Social and Economic Networks’. He also teaches an online course on networks and co-teaches two others on game theory. Jackson is a Member of the National Academy of Sciences and a Fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science, the Econometric Society, the Game Theory Society, and an Economic Theory Fellow. His other honors include a Guggenheim Fellowship, the Social Choice and Welfare Prize, the von Neumann Award from Rajk Laszlo College, an honorary doctorate from Aix-Marseille University, the Jean-Jacques Laffont Prize from the Toulouse School of Economics, the Slater Family Lecturer of the Year Prize from the Technion, the B.E.Press Arrow Prize for Senior Economists, the BBVA Frontiers of Knowledge Award in Economics, Finance, and Management, and teaching awards. He has served on the editorial boards of Econometrica, Games and Economic Behavior, PNAS, the Review of Economic Design, and as the President of the Game Theory Society.
Qiaozhu Mei
University of Michigan
https://websites.umich.edu/~qmei/
Bio: Qiaozhu is a professor in the School of Information and the Department of EECS at the University of Michigan. His research focuses on large-scale data mining, machine learning, information retrieval, and natural language processing, with broad applications to networks, Web, and healthcare. Qiaozhu is an ACM distinguished member (2017) and a recipient of the NSF Career Award (2011). His work has received multiple best paper awards at WWW, ICML, KDD, WSDM, and other major conferences. He is the founding director of the master degree of applied data science at the University of Michigan. He has rich experience organizing workshops and related events, including being the General Co-Chair of SIGIR 2018.
Yutong Xie
University of Michigan
https://yutxie.com/
Bio: Yutong Xie is a Ph.D. candidate and Barbour Scholar at the University of Michigan School of Information, advised by Prof. Qiaozhu Mei. Her research spans AI behavioral science, AI for science, and AI for creativity, with publications in top-tier venues like PNAS, ICLR, NeurIPS, AAAI, KDD, WWW, and NAACL. Yutong actively engages in the academic community, co-organizing workshops on AI behavioral science and graph learning, and serves as a regular reviewer for conferences such as NeurIPS, ICML, AAAI, KDD, and WWW. Her research has been recognized with awards including the Rising Stars in EECS, University of Michigan Barbour Scholarship, Gary M. Olson Outstanding Ph.D. Student Award, and D. E. Shaw Research Graduate Women’s Fellowship. Her work is supported by funding from NSF, LG Research, and Rackham Graduate School. She also collaborates with industry partners like Moblab, Niantic, and ByteDance. Prior to her doctoral studies at Michigan, Yutong earned her Bachelor’s degree from Shanghai Jiao Tong University as a member of the ACM Honors Class, where she was advised by Prof. Yong Yu and Prof. Weinan Zhang.
Walter Yuan
MobLab and Empathia AI
https://www.linkedin.com/in/walteryuan/
Bio: Walter Yuan is the CEO and co-founder of MobLab and Empathia AI. At MobLab, Walter spearheaded the development of a widely adopted platform for social science classroom experiments used by thousands of educational institutions globally. With a background in biology from the University of Wisconsin-Madison, he co-founded Empathia AI, combining AI expertise and clinical insights to create a medical copilot aimed at reducing physician burnout and improving patient care. Walter previously managed experimental laboratories at Caltech and UCLA, supporting a wide range of social science research. In his free time, he collaborates on research projects with colleagues from Stanford, Michigan, and other institutions.