Fairness-aware Machine Learning for Multi-task Learning and Domain Generalization
主讲人：美国贝勒大学（Baylor University）赵辰 助理教授
Dr. Zhao is an Assistant Professor at Department of Computer Science, Baylor University, Waco Texas. Prior to joining Baylor, he was a senior R&D computer vision engineer at Kitware Inc. Dr. Zhao received his doctoral degree in computer science from The University of Texas at Dallas in 2021. In 2016, he received dual M.S. degrees in computer science and biomedical science from University at Albany, SUNY and Albany Medical College, respectively. His works focus on Machine Learning, Deep Learning, Data Mining, and Computer Vision and they have been accepted and published in premier conferences, including KDD, CVPR, ICASSP, AAAI, WWW, ICDM, PAKDD, etc. Besides, Dr. Zhao served as Program Committee members of top international conferences, such as KDD, NeurIPS, AAAI, IJCAI, ICDM, BigData, ECMLPKDD, AISTATS, WSDM, WACV, etc. Homepage: https://charliezhaoyinpeng.github.io/homepage/
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 system 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 harming any 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 at last.