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Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness
Xueru Zhang · Mohammad Mahdi Khalili · Cem Tekin · Mingyan Liu

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #110

Machine Learning (ML) models trained on data from multiple demographic groups can inherit representation disparity (Hashimoto et al., 2018) that may exist in the data: the model may be less favorable to groups contributing less to the training process; this in turn can degrade population retention in these groups over time, and exacerbate representation disparity in the long run. In this study, we seek to understand the interplay between ML decisions and the underlying group representation, how they evolve in a sequential framework, and how the use of fairness criteria plays a role in this process. We show that the representation disparity can easily worsen over time under a natural user dynamics (arrival and departure) model when decisions are made based on a commonly used objective and fairness criteria, resulting in some groups diminishing entirely from the sample pool in the long run. It highlights the fact that fairness criteria have to be defined while taking into consideration the impact of decisions on user dynamics. Toward this end, we explain how a proper fairness criterion can be selected based on a general user dynamics model.

Author Information

Xueru Zhang (University of Michigan)
Mohammad Mahdi Khalili (university of michigan)
Cem Tekin (Bilkent University)

Cem is an Associate Professor in the Department of Electrical and Electronics Engineering and Head of Cognitive Systems, Bandits and Optimization Research Group (CYBORG) at Bilkent University. He received his PhD degree in Electrical Engineering: Systems from the University of Michigan, Ann Arbor, in 2013 (advisor: Mingyan Liu). He also received his MS degree in Mathematics and MSE degree in Electrical Engineering: Systems, from the University of Michigan in 2011 and 2010, respectively. Prior to attending the University of Michigan, He received his BS in Electrical and Electronics Engineering (valedictorian) from METU in 2008. From February 2013 to January 2015 he was a postdoctoral scholar in Electrical Engineering Department, UCLA (advisor: Mihaela van der Schaar). He received the Fred W. Ellersick award for the best paper in MILCOM 2009, the Science Academy Association of Turkey Distinguished Young Scientist (BAGEP) Award in 2019, Parlar Foundation Research Incentive Award in 2019, and IEEE Turkey Chapter Research Incentive Award in 2020. He is a Senior Member of IEEE. Cem has authored or coauthored over 60 research papers, 5 book chapters and a research monograph. He has served as a reviewer for numerous journals including IEEE Transactions on Information Theory, IEEE Transactions on Automatic Control, IEEE/ACM Transactions on Networking, IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing, IEEE Transactions on Mobile Computing, IEEE Transactions on Wireless Communications, IEEE JSTSP and IEEE JSAC. He has served as a reviewer for NeurIPS-22, ICML-22, ICLR-22, AISTATS-22, NeurIPS-21, ICML-21, AISTATS-21, NeurIPS-20, ICML-20 and TPC member for AAAI-21, AAAI-18, ACM Mobihoc-17, AAAI-17, AAAI-16, ISM-16, ECAI-16, MLSP-15 and GlobalSIP-15.

Mingyan Liu (university of Michigan, Ann Arbor)

Mingyan Liu (M'00, SM'11, F'14) received her Ph.D. Degree in electrical engineering from the University of Maryland, College Park, in 2000. She is currently a professor with the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, and the Peter and Evelyn Fuss Chair of Electrical and Computer Engineering. Her research interests are in optimal resource allocation, performance modeling, sequential decision and learning theory, game theory and incentive mechanisms, with applications to large-scale networked systems, cybersecurity and cyber risk quantification. She has served on the editorial boards of IEEE/ACM Trans. Networking, IEEE Trans. Mobile Computing, and ACM Trans. Sensor Networks. She is a Fellow of the IEEE and a member of the ACM.

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