Fair Interpretable Representation Learning with Correction Vectors

TL;DR

This paper introduces a novel fair representation learning framework using correction vectors, enhancing interpretability and achieving state-of-the-art fairness without sacrificing performance.

Contribution

It proposes a new correction vector-based approach for fair representation learning, including explicit and invertible models, improving interpretability and legal compliance.

Findings

Models do not lose ranking or classification accuracy

State-of-the-art fairness results achieved

Framework aligns with recent EU legislation

Abstract

Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information. Various representation debiasing techniques have been proposed in the literature. However, as neural networks are inherently opaque, these methods are hard to comprehend, which limits their usefulness. We propose a new framework for fair representation learning that is centered around the learning of "correction vectors", which have the same dimensionality as the given data vectors. Correction vectors may be computed either explicitly via architectural constraints or implicitly by training an invertible model based on Normalizing Flows. We show experimentally that several fair representation learning models constrained in such a way do not exhibit losses in ranking or classification performance. Furthermore, we demonstrate that state-of-the-art results can be achieved by the invertible model. Finally, we discuss the law standing of our methodology in light of recent legislation in the European Union.