The machine learning community has become alert to the ways that predictive algorithms
can inadvertently introduce unfairness in decision-making. Herein, we discuss how concepts …

The use of machine learning to guide clinical decision making has the potential to worsen
existing health disparities. Several recent works frame the problem as that of algorithmic …

Machine learning models are increasingly adopted for facilitating clinical decision-making.
However, recent research has shown that machine learning techniques may result in …

Machine learning is used increasingly in clinical care to improve diagnosis, treatment
selection, and health system efficiency. Because machine-learning models learn from …

In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …

In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Until now, much of the work on machine learning and health has focused on processes
inside the hospital or clinic. However, this represents only a narrow set of tasks and …

The use of machine learning systems to support decision making in healthcare raises
questions as to what extent these systems may introduce or exacerbate disparities in care …

The problem of algorithmic bias represents an ethical threat to the fair treatment of patients
when their care involves machine learning (ML) models informing clinical decision-making …

Recent attempts to achieve fairness in predictive models focus on the balance between
fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this …