Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure - CRISTAL-MAGNET
Pré-Publication, Document De Travail Année : 2024

Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure

Résumé

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.
Fichier principal
Vignette du fichier
2405.14521v1.pdf (588.37 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04863199 , version 1 (03-01-2025)

Licence

Identifiants

Citer

Gaurav Maheshwari, Aurélien Bellet, Pascal Denis, Mikaela Keller. Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure. 2024. ⟨hal-04863199⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

More