Conditional Supervised Contrastive Learning for Fair Text Classification

TL;DR

This paper introduces a contrastive learning approach for fair text classification that aims to balance accuracy and bias mitigation by satisfying equalized odds, supported by theoretical analysis and empirical validation.

Contribution

It proposes a novel use of conditional supervised contrastive objectives to learn fair representations for text classification, addressing bias issues in existing methods.

Findings

Effective in balancing task performance and bias mitigation.

Stable across different hyperparameter settings.

Outperforms existing baselines in experiments.

Abstract

Contrastive representation learning has gained much attention due to its superior performance in learning representations from both image and sequential data. However, the learned representations could potentially lead to performance disparities in downstream tasks, such as increased silencing of underrepresented groups in toxicity comment classification. In light of this challenge, in this work, we study learning fair representations that satisfy a notion of fairness known as equalized odds for text classification via contrastive learning. Specifically, we first theoretically analyze the connections between learning representations with a fairness constraint and conditional supervised contrastive objectives, and then propose to use conditional supervised contrastive objectives to learn fair representations for text classification. We conduct experiments on two text datasets to demonstrate the effectiveness of our approaches in balancing the trade-offs between task performance and bias mitigation among existing baselines for text classification. Furthermore, we also show that the proposed methods are stable in different hyperparameter settings.