2019-05-29 | Lingxiao Huang：Fairness in Automated Decision-Making Tasks
Automated decision-making algorithms are increasingly deployed and affect people's lives significantly. Recently, there has been growing concern about systematically discriminate against minority groups of individuals that may exist in such algorithms. Thus, developing algorithms that are "fair" with respect to sensitive attributes has become an important problem.
In this talk, I will first introduce the motivation of "fairness" in real-world applications and how to model "fairness" in theory. Then I will present several recent progress in designing algorithms that maintain fairness requirements for automated decision-making tasks, including multiwinner voting, personalization, classification, and clustering.
Lingxiao Huang is a postdoc of computer science in EPFL, where he is advised by Nisheeth Vishnoi. He joined EPFL in 2017, after received his Ph.D. in IIIS, Tsinghua University.
His current research interest is algorithm design in machine learning and social science. He is passionate about creating novel algorithms that are motivated by existing practical challenges.