Counterfactual fairness methods address the question: How would the
prediction change if the sensitive identity attributes referenced in the text
instance were different? These methods are entirely based on generating
counterfactuals for the given training and test set instances. Counterfactual
instances are commonly prepared by replacing sensitive identity terms, i.e.,
the identity terms present in the instance are replaced with other identity
terms that fall under the same sensitive category. Therefore, the efficacy of
these methods depends heavily on the quality and comprehensiveness of identity
pairs. In this paper, we offer a two-step data augmentation process where (1)
the former stage consists of a novel method for preparing a comprehensive list
of identity pairs with word embeddings, and (2) the latter consists of
leveraging prepared identity pairs list to enhance the training instances by
applying three simple operations (namely identity pair replacement, identity
term blindness, and identity pair swap). We empirically show that the two-stage
augmentation process leads to diverse identity pairs and an enhanced training
set, with an improved counterfactual token-based fairness metric score on two
well-known text classification tasks.

本文提出了一种两步数据增强过程，第一步是使用词嵌入方法准备身份对的全面列表，第二步是利用身份对列表通过三种简单的操作（即身份对替换、身份项盲目替换和身份对交换）增强训练实例，实验结果表明，这个增强过程可以产生多样化的身份对，并提高了基于令牌的反事实公平度量得分，适用于两个文本分类任务。