arXiv:2605.00185v2 Announce Type: replace 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 di
The increasing deployment of AI systems in real-world scenarios necessitates addressing fairness issues more rigorously, especially as the complexity and scale of datasets grow.
Ensuring fairness in AI models, particularly in data distillation, is critical for equitable outcomes across diverse user groups and for mitigating societal biases embedded in AI applications.
This research highlights a new, specific challenge in dataset distillation related to maintaining fairness across demographic subgroups, leading to a need for new distillation techniques.
- · AI fairness researchers
- · Developers of inclusive AI systems
- · Users from underrepresented demographic groups
- · AI models that rely on simplistic data distillation
- · Organizations deploying biased AI systems
- · Subgroups poorly represented in training data
Dataset distillation methods will need to integrate explicit fairness objectives from the outset to avoid performance degradation for specific groups.
New regulatory standards for AI fairness may emerge that require proof of equitable performance across demographic subgroups, even with compressed datasets.
The development of 'fair-by-design' AI systems could accelerate, leading to more trustworthy and widely accepted AI applications across all sectors.
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Read at arXiv cs.LG