Fair Representation Learning with Maximum Mean Discrepancy Distance Constraint (Student Abstract) - Lopotenco, Alexandru; Tong Pan, Ian; Zhang, Jack; Xiong Qiao, Guan (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
Unsupervised learning methods such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoding are regularly used in dimensionality reduction within the statistical learning scene. However, despite a pivot toward fairness and explainability in machine learning over the past few years, there have been few rigorous attempts toward a generalized framework of fair and explainable representation learning. Our paper explores the possibility of such a framework that leverages maximum mean discrepancy to remove information derived from a protected class from generated representations. For the optimization, we introduce a binary search component to optimize the Lagrangian coefficients. We present rigorous mathematical analysis and experimental results of our framework applied to t-SNE.