Fair Dataset Distillation via Cross-Group Barycenter Alignment

TL;DR

This paper introduces a method for fair dataset distillation that aligns subgroup representations to reduce bias and improve fairness in models trained on synthetic data.

Contribution

It proposes a novel barycenter alignment approach to mitigate subgroup disparities during dataset distillation, enhancing fairness.

Findings

Reduces fairness gaps in models trained on distilled datasets.

Compatible with existing distillation methods.

Empirically improves fairness metrics on benchmark datasets.

Abstract

Dataset Distillation aims to compress a large dataset into a small synthetic one while maintaining predictive performance. We show that as different demographic groups exhibit distinct predictive patterns, the distillation process struggles to simultaneously preserve informative signals for all subgroups, regardless of whether group sizes are mildly or severely imbalanced. Consequently, models trained on distilled data can experience substantial performance drops for certain subgroups, leading to fairness gaps. Crucially, these gaps do not disappear by merely correcting group imbalance, since they stem from fundamental mismatches in subgroup predictive patterns rather than from sample-size disparities alone. We therefore formally analyze the interaction between these two sources of bias and cast the solution as identifying a group-imbalance-agnostic barycenter of the predictive information that induces similar representations across all subgroups. By distilling toward this shared aggregate representation, we show that group fairness concerns can be reduced. Our approach is compatible with existing distillation methods, and empirical results show that it substantially reduces bias introduced by dataset distillation. Code is available at https://github.com/mhmoslemi/COBRA.