Department of Computer Science

Spring 2023 Symposium

Fairness-aware machine learning for multi-task learning and domain generalization

Nowadays, machine learning plays an increasingly prominent role in our life since decisions that humans once made are now delegated to automated systems. In recent years, an increasing number of reports stated that human bias is revealed in an artificial intelligence systems applied by high-tech companies. For example, Amazon has exposed a secret that its AI recruiting tool is biased against the minority. A critical component of developing responsible and trustworthy machine learning models is ensuring that such models are not unfairly population sub-groups. However, most of the existing fairness-aware algorithms focus on solving machine learning problems limited to either a single task or a static environment. How to learn a fair model (1) jointly with multiple biased tasks and/or (2) in changing environments are barely touched. In this talk, I will first focus on several selected published and ongoing works on the topic of fairness-aware machine learning with the setting of online/offline paradigms and static/changing environments. Then, some future directions and research works on other topics are introduced last.

Chen Zhao
March, 16, 7:00pm
Roddy 149 presented by
Dr. Chen Zhao



Dr. Chen Zhao is currently a senior research and development engineer at Kitware Inc. Prior to working at Kitware Inc, he received his doctoral degree in computer science from the University of Texas at Dallas in 2021, undre the supervision of Prof. Feng Chen. In 2016, he received dual M.S. degrees in computer science and biomedical science form University at Albany, SUNY, and Albany Medical College. His research focus is on Machine Learning, Deep Learning, Data Mining, and Computer Vision, specifically in Fairness-aware machine learning, novelty detection based on uncertainty quantification, and casual representation learning fir domain generalization. More details can be found on his homepage:


For further information contact Dr. Jingnan Xie