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

Abstract

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.

In the 33rd Conference on Neural Information Processing Systems (NeurIPS), 2019

Cited by in corpus (6)

• Fair Sequential Selection Using Supervised Learning Models
• Unintended Selection: Persistent Qualification Rate Disparities and Interventions
• Towards Return Parity in Markov Decision Processes
• Bandit Learning with Delayed Impact of Actions
• Identifying Best Fair Intervention
• Adaptive Data Debiasing through Bounded Exploration