Uncovering and Overcoming Implicit Bias in Data Pruning for Robust Deep Learning Models

Existing data pruning algorithms can produce highly biased classifiers, sacrificing performance on difficult classes to retain strong average accuracy. A fairness-aware pruning approach with random subsampling according to class-wise error rates can significantly improve the worst-class accuracy while maintaining high average performance.