Convex Formulations for Fair Principal Component Analysis

TL;DR

This paper introduces convex optimization methods to enhance fairness in PCA by ensuring protected class information cannot be inferred from reduced data, applicable to various datasets and clustering tasks.

Contribution

It proposes a novel fairness definition for PCA and develops convex SDP formulations to improve fairness in dimensionality reduction.

Findings

Convex SDP formulations effectively improve fairness in PCA.

The methods successfully applied to health data clustering.

Fair PCA reduces inference of protected attributes from reduced data.

Abstract

Though there is a growing body of literature on fairness for supervised learning, the problem of incorporating fairness into unsupervised learning has been less well-studied. This paper studies fairness in the context of principal component analysis (PCA). We first present a definition of fairness for dimensionality reduction, and our definition can be interpreted as saying that a reduction is fair if information about a protected class (e.g., race or gender) cannot be inferred from the dimensionality-reduced data points. Next, we develop convex optimization formulations that can improve the fairness (with respect to our definition) of PCA and kernel PCA. These formulations are semidefinite programs (SDP's), and we demonstrate the effectiveness of our formulations using several datasets. We conclude by showing how our approach can be used to perform a fair (with respect to age) clustering of health data that may be used to set health insurance rates.