Human-AI Interaction Under Societal Disagreement
Presented by Michael
Michael’s research focuses on social, societal, and interactive technologies, with work featured in The New York Times, TED AI, and MIT Technology Review. He has earned prestigious awards, including the Alfred P. Sloan Fellowship, the UIST Lasting Impact Award, and the Patrick J. McGovern Tech for Humanity Prize. Michael holds a master’s and Ph.D. in Computer Science from MIT.
Abstract
How can we better model human attitudes and behaviors? Traditional simulations often fail to capture the complexity of human behavior, but AI opens up new possibilities. In this talk, Michael will discuss generative agents—AI-driven simulations of human behavior that can remember, reflect, and plan. Grounded in qualitative data from over 1,000 Americans, these agents replicate survey responses with high accuracy. This research offers insights into designing more effective online social spaces, addressing societal disagreements in AI, and embedding societal values into algorithms.