  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.
