用分组专家模型提升皮肤疾病诊断公平性，不丢准确率。
Incorporating Rather Than Eliminating: Achieving Fairness for Skin Disease Diagnosis Through Group-Specific Expert
- 引入分层专家混合框架，按群体特征动态分配数据
- 在保持公平性的同时，准确率显著提升
- 适合关注医疗AI公平性的研究者与开发者
基于AI的皮肤疾病诊断系统虽已达到高准确率，但常在不同人口群体间表现出偏差，导致医疗结果不公和患者信任度下降。现有缓解偏差的方法多试图消除敏感属性与诊断预测间的关联，但常因丢失临床相关诊断线索而降低性能。本文提出一种新思路：不消除敏感属性，而是将其纳入模型设计。我们提出FairMoE框架，采用分层专家混合模块作为分组特定学习器。不同于传统方法中固定按群体标签分配数据，FairMoE动态将数据路由至最合适的专家，尤其擅长处理群体边界附近的案例。实验表明，相比以往方法会降低性能，FairMoE在保持相当公平性指标的同时，实现了显著的准确率提升。
原文摘要 · Abstract (English)
AI-based systems have achieved high accuracy in skin disease diagnostics but often exhibit biases across demographic groups, leading to inequitable healthcare outcomes and diminished patient trust. Most existing bias mitigation methods attempt to eliminate the correlation between sensitive attributes and diagnostic prediction, but those methods often degrade performance due to the lost of clinically relevant diagnostic cues. In this work, we propose an alternative approach that incorporates sensitive attributes to achieve fairness. We introduce FairMoE, a framework that employs layer-wise mixture-of-experts modules to serve as group-specific learners. Unlike traditional methods that rigidly assign data based on group labels, FairMoE dynamically routes data to the most suitable expert, making it particularly effective for handling cases near group boundaries. Experimental results show that, unlike previous fairness approaches that reduce performance, FairMoE achieves substantial accuracy improvements while preserving comparable fairness metrics.
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