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

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 …

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 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 …

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the
fairness of such data-driven insights remains a concern in high-stakes fields. Despite …

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 …

Objectives Fairness is a core concept meant to grapple with different forms of discrimination
and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet …

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 …

A growing body of work uses the paradigm of algorithmic fairness to frame the development
of techniques to anticipate and proactively mitigate the introduction or exacerbation of health …