Conditional Supervised Contrastive Learning for Fair Text Classification
This work addresses fairness issues in text classification for underrepresented groups, representing an incremental improvement over existing baselines.
The paper tackles the problem of performance disparities in text classification caused by contrastive representation learning, proposing a method to learn fair representations that satisfy equalized odds, and demonstrates its effectiveness in balancing task performance and bias mitigation on two text datasets.
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.