A tutorial on fairness in machine learning in healthcare
arxiv(2024)
Abstract
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.
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.
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.
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