Taking Advantage of Multitask Learning for Fair Classification

TL;DR

This paper introduces a multitask learning approach with fairness constraints to improve classification accuracy and fairness without directly using sensitive features, addressing bias and privacy concerns.

Contribution

It proposes a novel method combining multitask learning and sensitive feature prediction to enhance fairness and accuracy without explicit sensitive attribute use.

Findings

Significant improvements in fairness metrics.

Enhanced classification accuracy across groups.

Effective privacy protection of sensitive features.

Abstract

A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information (e.g., gender or ethnic group) as part of a statistical model, and any commitment to protect these characteristics. Often, due to biases present in the data, using the sensitive information in the functional form of a classifier improves classification accuracy. In this paper we show how it is possible to get the best of both worlds: optimize model accuracy and fairness without explicitly using the sensitive feature in the functional form of the model, thereby treating different individuals equally. Our method is based on two key ideas. On the one hand, we propose to use Multitask Learning (MTL), enhanced with fairness constraints, to jointly learn group specific classifiers that leverage information between sensitive groups. On the other hand, since learning group specific models might not be permitted, we propose to first predict the sensitive features by any learning method and then to use the predicted sensitive feature to train MTL with fairness constraints. This enables us to tackle fairness with a three-pronged approach, that is, by increasing accuracy on each group, enforcing measures of fairness during training, and protecting sensitive information during testing. Experimental results on two real datasets support our proposal, showing substantial improvements in both accuracy and fairness.