On the Fairness of Disentangled Representations
This addresses fairness issues in machine learning for scenarios where sensitive variables are not directly observable, though it is incremental as it builds on existing disentanglement methods.
The paper investigates whether disentangled representations can improve fairness in downstream prediction tasks, finding that some disentanglement scores correlate with increased fairness when sensitive variables are unobserved, based on analysis of over 12,600 trained models.
Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for improving the fairness of downstream prediction tasks based on representations. We consider the setting where the goal is to predict a target variable based on the learned representation of high-dimensional observations (such as images) that depend on both the target variable and an \emph{unobserved} sensitive variable. We show that in this setting both the optimal and empirical predictions can be unfair, even if the target variable and the sensitive variable are independent. Analyzing the representations of more than \num{12600} trained state-of-the-art disentangled models, we observe that several disentanglement scores are consistently correlated with increased fairness, suggesting that disentanglement may be a useful property to encourage fairness when sensitive variables are not observed.