  $\textbf{OBJECTIVE}$: 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.
