Explanations of AI predictions are considered crucial for human-AI interactions. I argue that successful human-AI interactions require two steps: AI explanation and human interpretation. Therefore, effective explanations necessitates the understanding of human interpretation. In this talk, I will present our work to address this challenge through human-centered evaluation and generation of explanations. First, I will discuss the distinction between emulation and discovery tasks, which shapes human interpretation. In emulation tasks, humans provide groundtruth labels and the goal of AI is to emulate human intelligence. While it may seem intuitive that humans can provide valid explanations in this case, I argue that humans may not be able to provide "good" explanations. Caution is thus required to use human explanations for evaluation or as supervision signals despite the growing efforts in building datasets of human explanations. In contrast, in discovery tasks, humans may not necessarily know the groundtruth label. Human-subject experiments show that explanations fail to improve human decisions, namely, human+AI rarely outperforms AI alone. I will highlight the importance of identifying human strengths and AI strengths, and introduce decision-focused summarization. To conclude, I will return to classic work on human-computer symbiosis and speculate about the future of human-AI interaction in the era of *GPT*.
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 human-centered AI, natural language processing, 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 Google research scholar award, research awards from Amazon, IBM, JP Morgan, and Salesforce, a Facebook fellowship, and a Yahoo! Key Scientific Challenges award.