PhD Candidate
University of Michigan
Areas of Interest
- Artificial Intelligence
- Security
- Trustworthy Machine Learning
Poster
How Do Fair Decisions Fare in Long-TermQualification?
Abstract
Although many fairness criteria have been proposed for decision making, their long-term impact on the well-being of a population remains unclear. In this work, we study the dynamics of population qualification and algorithmic decisions under a partially observed Markov decision problem setting. By characterizing the equilibrium of such dynamics, we analyze the long-term impact of static fairness constraints on the equality and improvement of group well-being. Our results show that static fairness constraints can either promote equality or exacerbate disparity depending on the driving factor of qualification transitions and the effect of sensitive attributes on feature distributions. We also consider possible interventions that can effectively improve group qualification or promote equality of group qualification. Our theoretical results and experiments on static real-world datasets with simulated dynamics show that our framework can be used to facilitate social science studies.
Bio
Xueru Zhang is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of Michigan, advised by Mingyan Liu. She received her M.Sc. degree in Electrical and Computer Engineering from the University of Michigan in 2016 and B.Eng. degree in Electronic and Information Engineering from Beihang University (BUAA), Beijing, China, in 2015. Her research lies at the intersection of machine learning, optimization, statistics and economics, including topics such as data privacy, algorithmic fairness and security economics. She was a recipient of Rackham Predoctoral Fellowship in 2020.
