  While interest in the application of machine learning to improve healthcare
has grown tremendously in recent years, a number of barriers prevent deployment
in medical practice. A notable concern is the potential to exacerbate
entrenched biases and existing health disparities in society. The area of
fairness in machine learning seeks to address these issues of equity; however,
appropriate approaches are context-dependent, necessitating domain-specific
consideration. We focus on clinical trials, i.e., research studies conducted on
humans to evaluate medical treatments. Clinical trials are a relatively
under-explored application in machine learning for healthcare, in part due to
complex ethical, legal, and regulatory requirements and high costs. Our aim is
to provide a multi-disciplinary assessment of how fairness for machine learning
fits into the context of clinical trials research and practice. We start by
reviewing the current ethical considerations and guidelines for clinical trials
and examine their relationship with common definitions of fairness in machine
learning. We examine potential sources of unfairness in clinical trials,
providing concrete examples, and discuss the role machine learning might play
in either mitigating potential biases or exacerbating them when applied without
care. Particular focus is given to adaptive clinical trials, which may employ
machine learning. Finally, we highlight concepts that require further
investigation and development, and emphasize new approaches to fairness that
may be relevant to the design of clinical trials.

Machine Learning;Computers and Society