Abstract Machine learning (ML) holds great promise for improving healthcare, but it is critical
to ensure that its use will not propagate or amplify health disparities. An important step is to …

Benefiting from the digitization of healthcare data and the development of computing power,
machine learning methods are increasingly used in the healthcare domain. Fairness …

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 …

Common fairness definitions in machine learning focus on balancing notions of disparity
and utility. In this work, we study fairness in the context of risk disparity among sub …

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

A central goal of algorithmic fairness is to reduce bias in automated decision making. An
unavoidable tension exists between accuracy gains obtained by using sensitive information …

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 …

As machine learning methods gain prominence within clinical decision-making, the need to
address fairness concerns becomes increasingly urgent. Despite considerable work …

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 …

Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …