A tutorial on fairness in machine learning in healthcare
<|reference_start|>A tutorial on fairness in machine learning in healthcare: $\textbf{OBJECTIVE}$: 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.<|reference_end|>
@article{gao2024what,
    title={What is Fair? Defining Fairness in Machine Learning for Health},
    author={Jianhui Gao, Benson Chou, Zachary R. McCaw, Hilary Thurston, Paul
  Varghese, Chuan Hong, Jessica Gronsbell},
    journal={arXiv preprint arXiv:2406.09307},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09307},
    primaryClass={cs.LG cs.CY stat.ML}
}