The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Yongkai Wu on Wednesday, March 18, 2020, at 3:00 p.m., in Chem Sci 101. Wu’s talk is titled, “Achieving Causal Fairness in Machine Learning.”
Wu is a Ph.D. candidate in the Department of Computer Science and Computer Engineering at the University of Arkansas. He received his B.Eng. degree in electronic engineering from Tsinghua University, China, in 2014. His research interests focus on machine learning, data mining, and artificial intelligence, particularly fairness-aware machine learning and causal inference.
Fairness in AI systems is receiving increasing attention. Fairness-aware machine learning studies the problem of building machine learning models that are subject to fairness requirements. In this talk, Hu will present his dissertation research on developing a causality-based framework for measuring discrimination and achieving fairness in classification.
In his research, Wu and his colleagues formulate discrimination based on the causal inference framework where the causal effect is measured from a causal graph and observed data. The research proposes a unified definition that covers most of previous causality-based fairness notions, namely the path-specific counterfactual fairness (PC fairness), targeting an inherent challenge in causal inference, unidentification, which means some causal quantities cannot be uniquely computed from observed data. To overcome this challenge, the research proposes novel estimation methods to bound the unidentifiable fairness quantities, and develops an efficient post-processing method to achieve fairness in unidentifiable counterfactual cases.
Wu will also briefly introduce his work dealing with discrimination issues in various machine learning tasks and applications, and discuss future research directions on fairness-aware machine learning and FATE (Fairness, Accountability, Transparency, and Explainability) in AI.
Wu’s publications have appeared in prestigious conferences including IJCAI, KDD, NeurIPS, WWW, and a premier journal TKDE. He has served as a PC member for several international conferences including AAAI, IJCAI, KDD, PAKDD.