CIIR Talk Series

Title: Explainable Human-centered AI

Abstract: AI has been an important part in many human-centered tasks such as search, recommendation, dialog systems and social networks. However, how to understand and interpret the results produced by AI remains a significant challenge, which greatly influences the trust between humans and AI. In this talk, we will introduce Explainable AI from both technical and application perspectives, including explainable machine learning based on neural logic and neural symbolic reasoning, causal and counterfactual reasoning, knowledge graph reasoning, explainable graph neural networks, techniques for generating natural language explanations, and their application in search, recommendation and dialog systems.

Bio: Yongfeng Zhang is an Assistant Professor in the Department of Computer Science at Rutgers University and directs the Web Intelligent Systems and Economics (WISE) lab. His research interest is in Information Retrieval, Recommender Systems, Economics of Data Science, Explainable AI, Fairness in AI, and AI Ethics. In the previous he was a postdoc advised by Prof. W. Bruce Croft in the Center for Intelligent Information Retrieval (CIIR) at UMass Amherst, and did his PhD and BE in Computer Science at Tsinghua University, with a BS in Economics at Peking University. He is a Siebel Scholar of the class 2015.

To attend this talk via Zoom, click here. Participants will need a passcode to attend this event. If you need the passcode for this series, please see the event advertisement on the seminars email list or reach out to Alex Taubman. For any other questions about this event with the Center for Intelligent Information Retrieval, please contact Jean Joyce.