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 issue of bias and fairness in healthcare has been around for centuries. With the
integration of AI in healthcare the potential to discriminate and perpetuate unfair and biased …

Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …

Abstract Machine learning and artificial intelligence are increasingly deployed in critical
societal functions such as finance, media and healthcare. Along with their deployment come …

We provide an overview of the state-of-the-art in fairnessaware machine learning and
examine a wide variety of research articles in the area. We survey different fairness notions …

Extensive efforts have been made to understand and improve the fairness of machine
learning models based on observational metrics, especially in high-stakes domains such as …

With growing machine learning (ML) applications in healthcare, there have been calls for
fairness in ML to understand and mitigate ethical concerns these systems may pose …

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 …

Algorithms are now routinely used to make consequential decisions that affect human lives.
Examples include college admissions, medical interventions or law enforcement. While …

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 …