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

Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness