In this paper, we introduce a data augmentation approach specifically tailored to enhance intersectional fairness in classification tasks. Our method capitalizes on the hierarchical structure inherent to intersectionality, by viewing groups as intersections of their parent categories. This perspective allows us to augment data for smaller groups by learning a transformation function that combines data from these parent groups. Our empirical analysis, conducted on four diverse datasets including both text and images, reveals that classifiers trained with this data augmentation approach achieve superior intersectional fairness and are more robust to ``leveling down'' when compared to methods optimizing traditional group fairness metrics.

翻译：本文提出一种专门针对分类任务中交叉公平性增强的数据增强方法。我们的方法利用交叉性固有的层次结构，将群体视为其父类别的交集。这一视角使我们能够通过学习一个转换函数来组合父类别的数据，从而为较小群体生成增强数据。我们在包含文本和图像的四类不同数据集上进行的实证分析表明，与优化传统群体公平性指标的方法相比，采用此数据增强方法训练的分类器在交叉公平性方面表现更优，且对"水平下降"现象具有更强的鲁棒性。