Deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated exceptional performance in diagnosing skin diseases, often outperforming dermatologists. However, they have also unveiled biases linked to specific demographic traits, notably concerning diverse skin tones or gender, prompting concerns regarding fairness and limiting their widespread deployment. Researchers are actively working to ensure fairness in AI-based solutions, but existing methods incur an accuracy loss when striving for fairness. To solve this issue, we propose a `two-biased teachers' (i.e., biased on different sensitive attributes) based approach to transfer fair knowledge into the student network. Our approach mitigates biases present in the student network without harming its predictive accuracy. In fact, in most cases, our approach improves the accuracy of the baseline model. To achieve this goal, we developed a weighted loss function comprising biasing and debiasing loss terms. We surpassed available state-of-the-art approaches to attain fairness and also improved the accuracy at the same time. The proposed approach has been evaluated and validated on two dermatology datasets using standard accuracy and fairness evaluation measures. We will make source code publicly available to foster reproducibility and future research.

翻译：深度学习模型，特别是卷积神经网络（CNN），在皮肤病诊断中展现出卓越性能，往往超越皮肤科医生。然而，这些模型也暴露出与特定人口统计学特征相关的偏见，特别是在不同肤色或性别方面，引发了对公平性的担忧并限制了其广泛部署。研究人员正积极致力于确保基于AI的解决方案的公平性，但现有方法在追求公平性时会导致准确性损失。为解决这一问题，我们提出了一种基于“双偏向教师”（即在不同敏感属性上存在偏向）的方法，将公平知识迁移至学生网络。我们的方法在不损害预测准确性的前提下减轻了学生网络中存在的偏见。事实上，在大多数情况下，我们的方法提升了基线模型的准确性。为实现这一目标，我们开发了一个包含偏向损失项和去偏损失项的加权损失函数。我们超越了现有最先进的公平性方法，同时提升了准确性。所提出的方法已在两个皮肤病学数据集上，使用标准准确性和公平性评估指标进行了评估和验证。我们将公开源代码以促进可复现性和未来研究。