Fairness approaches in machine learning should involve more than an assessment of
performance metrics across groups. Shifting the focus away from model metrics, we reframe …

Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially
for those societal applications, eg, hiring systems should recommend applicants equally …

Deep learning has become a popular tool for computer-aided diagnosis using medical
images, sometimes matching or exceeding the performance of clinicians. However, these …

Research in machine learning fairness has historically considered a single binary
demographic attribute; however, the reality is of course far more complicated. In this work …

Decision-making systems based on AI and machine learning have been used throughout a
wide range of real-world scenarios, including healthcare, law enforcement, education, and …

The integration of artificial intelligence (AI) into healthcare promises groundbreaking
advancements in patient care, revolutionizing clinical diagnosis, predictive medicine, and …

Algorithmic systems are known to impact marginalized groups severely, and more so, if all
sources of bias are not considered. While work in algorithmic fairness to-date has primarily …

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

There is ample evidence to demonstrate that discrimination against several population
subgroups interferes with their ability to receive optimal surgical care. This bias can take …

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 …