Machine learning (ML) is increasingly being used to make decisions in our society. ML
models, however, can be unfair to certain demographic groups (eg, African Americans or …

Artificial intelligence holds great promise for improved health‐care outcomes. But it also
poses substantial new hazards, including algorithmic discrimination. For example, an …

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 …

Training and evaluation of fair classifiers is a challenging problem. This is partly due to the
fact that most fairness metrics of interest depend on both the sensitive attribute information …

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 …

One of the difficulties of artificial intelligence is to ensure that model decisions are fair and
free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and …

Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

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

Fairness in deep learning has attracted tremendous attention recently, as deep learning is
increasingly being used in high-stake decision making applications that affect individual …

Medical AI has tremendous potential to advance healthcare by supporting the evidence-
based practice of medicine, personalizing patient treatment, reducing costs, and improving …