This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …

Recent text-to-image matching models apply contrastive learning to large corpora of
uncurated pairs of images and sentences. While such models can provide a powerful score …

An outlier is an event or observation that is defined as an unusual activity, intrusion, or a
suspicious data point that lies at an irregular distance from a population. The definition of an …

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 …

Accuracy and individual fairness are both crucial for trustworthy machine learning, but these
two aspects are often incompatible with each other so that enhancing one aspect may …

Objective: In this paper, we explore the correlation between performance reporting and the
development of inclusive AI solutions for biomedical problems. Our study examines the …

With the increased use of machine learning algorithms to make decisions which impact
people's lives, it is of extreme importance to ensure that predictions do not prejudice …

Recent research has shown that seemingly fair machine learning models, when used to
inform decisions that have an impact on peoples' lives or well-being (eg, applications …

Online social media (OSM) has become an integral part of an individual's daily life. The
extensive computational power and decision-making ability of artificial intelligence (AI) and …

Explanations are believed to aid understanding of AI models, but do they affect users'
perceptions and trust in AI, especially in the presence of algorithmic bias? If so, when should …