FairDi trains biased per-group teachers and distills them into one student, reporting better accuracy-fairness trade-offs than prior methods on medical imaging benchmarks.
Ethical machine learning in healthcare
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Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging
FairDi trains biased per-group teachers and distills them into one student, reporting better accuracy-fairness trade-offs than prior methods on medical imaging benchmarks.