$\textbf{OBJECTIVE}$: Ensuring that machine learning (ML) algorithms are safe and effective within all patient groups, and do not disadvantage particular patients, is essential to clinical decision making and preventing the reinforcement of existing healthcare inequities. The objective of this tutorial is to introduce the medical informatics community to the common notions of fairness within ML, focusing on clinical applications and implementation in practice. $\textbf{TARGET AUDIENCE}$: As gaps in fairness arise in a variety of healthcare applications, this tutorial is designed to provide an understanding of fairness, without assuming prior knowledge, to researchers and clinicians who make use of modern clinical data. $\textbf{SCOPE}$: We describe the fundamental concepts and methods used to define fairness in ML, including an overview of why models in healthcare may be unfair, a summary and comparison of the metrics used to quantify fairness, and a discussion of some ongoing research. We illustrate some of the fairness methods introduced through a case study of mortality prediction in a publicly available electronic health record dataset. Finally, we provide a user-friendly R package for comprehensive group fairness evaluation, enabling researchers and clinicians to assess fairness in their own ML work.

翻译：$\textbf{目的}$：确保机器学习（ML）算法在所有患者群体中安全有效，且不会对特定患者造成不利影响，这对于临床决策制定和防止现有医疗不平等现象的加剧至关重要。本教程旨在向医学信息学界介绍机器学习中常见的公平性概念，重点关注临床应用与实践实施。$\textbf{目标受众}$：鉴于公平性差距出现在各类医疗应用中，本教程旨在为利用现代临床数据的研究人员和临床医生提供对公平性的理解，无需预设先验知识。$\textbf{范围}$：我们描述了用于定义机器学习公平性的基本概念和方法，包括概述医疗领域模型可能不公平的原因、总结并比较用于量化公平性的指标，以及讨论一些正在进行的研究。我们通过一个公开电子健康记录数据集中死亡率预测的案例研究，阐释了部分引入的公平性方法。最后，我们提供了一个用户友好的R软件包，用于全面的群体公平性评估，使研究人员和临床医生能够评估其自身机器学习工作的公平性。