Joint Adversarial Learning for Cross-domain Fair Classification

by   Yueqing Liang, et al.

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 develop 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 preprocess the data, regularize the model learning or postprocess 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 the 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 simultaneously estimate the sensitive attributes while learning a fair classifier 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.


page 1

page 2

page 3

page 4


You Can Still Achieve Fairness Without Sensitive Attributes: Exploring Biases in Non-Sensitive Features

Though machine learning models are achieving great success, ex-tensive s...

Rényi Fair Inference

Machine learning algorithms have been increasingly deployed in critical ...

Transfer of Machine Learning Fairness across Domains

If our models are used in new or unexpected cases, do we know if they wi...

Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics

Deep learning-based recognition systems are deployed at scale for severa...

Learning to Ignore: Fair and Task Independent Representations

Training fair machine learning models, aiming for their interpretability...

Fairness and Accuracy under Domain Generalization

As machine learning (ML) algorithms are increasingly used in high-stakes...

Exploiting Fairness to Enhance Sensitive Attributes Reconstruction

In recent years, a growing body of work has emerged on how to learn mach...

Please sign up or login with your details

Forgot password? Click here to reset