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.
翻译:由于在从图像和相继数据中学习表现方面表现优异,相互对立的表述学习受到重视。然而,所学的表述有可能导致下游任务的业绩差异,例如毒性评论分类中代表不足的群体更加沉默。鉴于这一挑战,我们在这项工作中研究的是公平表述,这种表述符合公平概念,即通过对比学习实现文本分类的均等性差。具体地说,我们首先从理论上分析学习表述与公平限制和有条件的受监督对比目标之间的联系,然后提议使用有条件的受监督对比目标来学习文本分类的公平表述。我们实验了两个文本数据集,以表明我们在平衡现有文本分类基线之间任务业绩平衡和减少偏差之间平衡的方法的有效性。此外,我们还表明,拟议方法在不同超参数环境中是稳定的。