Fairness has become increasingly pivotal in medical image recognition. However, without mitigating bias, deploying unfair medical AI systems could harm the interests of underprivileged populations. In this paper, we observe that while features extracted from the deeper layers of neural networks generally offer higher accuracy, fairness conditions deteriorate as we extract features from deeper layers. This phenomenon motivates us to extend the concept of multi-exit frameworks. Unlike existing works mainly focusing on accuracy, our multi-exit framework is fairness-oriented; the internal classifiers are trained to be more accurate and fairer, with high extensibility to apply to most existing fairness-aware frameworks. During inference, any instance with high confidence from an internal classifier is allowed to exit early. Experimental results show that the proposed framework can improve the fairness condition over the state-of-the-art in two dermatological disease datasets.

翻译：公平性在医学图像识别中已变得日益关键。然而，若不加干预地部署有偏见的非公平医学人工智能系统，可能会损害弱势群体的利益。本文观察到，尽管从神经网络深层提取的特征通常具有更高的准确性，但随着从更深层提取特征，公平性条件会逐渐恶化。这一现象促使我们扩展多出口框架的概念。与现有主要关注准确性的研究不同，我们的多出口框架以公平性为导向：内部分类器被训练为更准确且更公平，并具有高度的可扩展性，可应用于大多数现有的公平性感知框架。在推理过程中，任何内部分类器输出高置信度的实例均可提前退出。实验结果表明，所提出的框架在两个皮肤病数据集上能够改善现有技术水平的公平性条件。