University of Chicago
Date: 14 March 2022
Title: Towards Human-Centered AI: Complementing Humans with AI
Abstract: AI plays an increasingly prominent role in decision making for societally critical domains such as criminal justice, healthcare, and content moderation. However, human+AI rarely outperforms AI alone in human-AI decision making. In this talk, I will discuss possible factors at play and then present our two recent efforts in complementing humans with AI. First, I will introduce a novel problem, decision-focused summarization, where the goal is to summarize relevant information for a decision. Our algorithm, DecSum, is the only method that enables humans to outperform random chance in predicting which restaurant will be better rated in the future. Second, I will propose conditional delegation as an alternative paradigm for human-AI collaboration where humans create rules to indicate trustworthy regions of a model. Using content moderation as a testbed, we show that human+AI achieves higher precision than either human alone or AI alone. I will conclude with future directions for developing effective human-centered AI.
Speaker Biography: Chenhao Tan is an assistant professor of computer science at the University of Chicago, and is also affiliated with the Harris School of Public Policy. He obtained his PhD degree in the Department of Computer Science at Cornell University and bachelor's degrees in computer science and in economics from Tsinghua University. Prior to joining the University of Chicago, he was an assistant professor at the University of Colorado Boulder and a postdoc at the University of Washington. His research interests include natural language processing, human-centered AI, and computational social science. His work has been covered by many news media outlets, such as the New York Times and the Washington Post. He also won an NSF CAREER award, an NSF CRII award, a Salesforce research award, an Amazon research award, a Facebook fellowship, and a Yahoo! Key Scientific Challenges award.