Dataset distillation introduces fairness gaps from subgroup pattern mismatches rather than just imbalance; distilling to a group-agnostic barycenter of predictive information reduces these gaps.
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Causality resolves trade-offs in trustworthy AI by treating them as invariance conflicts under different data-generating process changes.
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Fair Dataset Distillation via Cross-Group Barycenter Alignment
Dataset distillation introduces fairness gaps from subgroup pattern mismatches rather than just imbalance; distilling to a group-agnostic barycenter of predictive information reduces these gaps.