Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach

TL;DR

This paper introduces a domain adaptation framework that leverages auxiliary domain information to improve fair classification in the target domain without requiring sensitive attributes, addressing privacy and data availability issues.

Contribution

It proposes a novel dual adversarial learning approach for fair classification that exploits source domain sensitive attributes to enhance fairness in the target domain.

Findings

Effective fair classification achieved without target domain sensitive attributes

Outperforms existing methods on real-world datasets

Demonstrates robustness in privacy-constrained scenarios

Abstract

Modern machine learning (ML) models are becoming increasingly popular and are widely used in decision-making systems. However, studies have shown critical issues of ML discrimination and unfairness, which hinder their adoption on high-stake applications. Recent research on fair classifiers has drawn significant attention to developing effective algorithms to achieve fairness and good classification performance. Despite the great success of these fairness-aware machine learning models, most of the existing models require sensitive attributes to pre-process the data, regularize the model learning or post-process the prediction to have fair predictions. However, sensitive attributes are often incomplete or even unavailable due to privacy, legal or regulation restrictions. Though we lack the sensitive attribute for training a fair model in the target domain, there might exist a similar domain that has sensitive attributes. Thus, it is important to exploit auxiliary information from a similar domain to help improve fair classification in the target domain. Therefore, in this paper, we study a novel problem of exploring domain adaptation for fair classification. We propose a new framework that can learn to adapt the sensitive attributes from a source domain for fair classification in the target domain. Extensive experiments on real-world datasets illustrate the effectiveness of the proposed model for fair classification, even when no sensitive attributes are available in the target domain.