Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting
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arXiv
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| Format: | Preprint |
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2025
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| author | Paxton, Kuniko Dehghani, Zeinab Aslansefat, Koorosh Thakker, Dhavalkumar Papadopoulos, Yiannis |
| author_facet | Paxton, Kuniko Dehghani, Zeinab Aslansefat, Koorosh Thakker, Dhavalkumar Papadopoulos, Yiannis |
| contents | Skin color has historically been a focal point of discrimination, yet fairness research in machine learning for medical imaging often relies on coarse subgroup categories, overlooking individual-level variations. Such group-based approaches risk obscuring biases faced by outliers within subgroups. This study introduces a distribution-based framework for evaluating and mitigating individual fairness in skin lesion classification. We treat skin tone as a continuous attribute rather than a categorical label, and employ kernel density estimation (KDE) to model its distribution. We further compare twelve statistical distance metrics to quantify disparities between skin tone distributions and propose a distance-based reweighting (DRW) loss function to correct underrepresentation in minority tones. Experiments across CNN and Transformer models demonstrate: (i) the limitations of categorical reweighting in capturing individual-level disparities, and (ii) the superior performance of distribution-based reweighting, particularly with Fidelity Similarity (FS), Wasserstein Distance (WD), Hellinger Metric (HM), and Harmonic Mean Similarity (HS). These findings establish a robust methodology for advancing fairness at individual level in dermatological AI systems, and highlight broader implications for sensitive continuous attributes in medical image analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08733 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting Paxton, Kuniko Dehghani, Zeinab Aslansefat, Koorosh Thakker, Dhavalkumar Papadopoulos, Yiannis Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Skin color has historically been a focal point of discrimination, yet fairness research in machine learning for medical imaging often relies on coarse subgroup categories, overlooking individual-level variations. Such group-based approaches risk obscuring biases faced by outliers within subgroups. This study introduces a distribution-based framework for evaluating and mitigating individual fairness in skin lesion classification. We treat skin tone as a continuous attribute rather than a categorical label, and employ kernel density estimation (KDE) to model its distribution. We further compare twelve statistical distance metrics to quantify disparities between skin tone distributions and propose a distance-based reweighting (DRW) loss function to correct underrepresentation in minority tones. Experiments across CNN and Transformer models demonstrate: (i) the limitations of categorical reweighting in capturing individual-level disparities, and (ii) the superior performance of distribution-based reweighting, particularly with Fidelity Similarity (FS), Wasserstein Distance (WD), Hellinger Metric (HM), and Harmonic Mean Similarity (HS). These findings establish a robust methodology for advancing fairness at individual level in dermatological AI systems, and highlight broader implications for sensitive continuous attributes in medical image analysis. |
| title | Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.08733 |