Fair Curriculum Learning for Concept Bottleneck Models in Dermatology

M.J. Cockayne, M. Ortolani, B. Al-Bander

MICCAI 2026, 2026

Abstract

Black box deep learning models for dermatology show performance disparities across skin types, with darker-skinned patients experiencing lower diagnostic performance. Concept Bottleneck Models (CBMs) offer interpretability through intermediate concept predictions but lack explicit fairness mechanisms. We introduce Fair Curriculum CBM, a four-phase curriculum learning approach for in-training bias mitigation that, instead of relying on concept difficulty, orders training by fairness objectives, including balanced foundation, demographic parity, equalized odds, and performance parity. This progressive structure trains all concepts jointly while gradually introducing fairness constraints and adversarial debiasing directly, rather than treating fairness as a post-hoc adjustment. On SkinCon (3230 images, 6 Fitzpatrick types), Fair Curriculum CBM improves lowest-group F1 by 63% (0.270 to 0.441, p<0.001) and reduces performance gaps by 44% (0.361 to 0.203, p=0.003) compared to difficulty-based curriculum learning, with simultaneous 5.3% overall F1 gain (p<0.001). Paired t-tests comparing fairness-first to difficulty-based curriculum approaches show significant improvements on diverse skin types (Types II, V, VI: p<=0.005) without degradation on lighter types. Results from 100 independent runs per model validate that fairness curriculum learning improves rather than constrains performance. Full code and models will be made available.

Status

This paper has been submitted to MICCAI 2026 and is currently under review.

Code and models will be released upon acceptance.

Links

Code

Fairness Concept Bottleneck Models Curriculum Learning Medical Imaging Dermatology Responsible AI

Citation

@inproceedings{cockayne2026faircurriculum,
  title={Fair Curriculum Learning for Concept Bottleneck Models in Dermatology},
  author={Cockayne, Matthew J. and Ortolani, Marco and Al-Bander, Baidaa},
  booktitle={Medical Image Computing and Computer Assisted Intervention (MICCAI)},
  year={2026}
}