The emphasis on fairness in predictive healthcare modeling has increased in popularity as
an approach for overcoming biases in automated decision-making systems. The aim is to …

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 …

In data collection for predictive modeling, underrepresentation of certain groups, based on
gender, race/ethnicity, or age, may yield less accurate predictions for these groups …

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 …

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 …

With the growing utilization of machine learning in healthcare, there is increasing potential to
enhance healthcare outcomes. However, this also brings the risk of perpetuating biases in …

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 often personalized with categorical attributes that are
protected, sensitive, self-reported, or costly to acquire. In this work, we show models that are …

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

Objective The integration of artificial intelligence (AI) and machine learning (ML) in health
care to aid clinical decisions is widespread. However, as AI and ML take important roles in …