In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

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 …

When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

Abstract Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving
graph analytical problems in various real-world applications. Nevertheless, GNNs could …

In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …

In this critical survey, we analyze typical claims on the relationship between explainable AI
(XAI) and fairness to disentangle the multidimensional relationship between these two …

Machine learning models are deployed as a central component in decision making and
policy operations with direct impact on individuals' lives. In order to act ethically and comply …

In credit markets, screening algorithms aim to discriminate between good-type and bad-type
borrowers. However, when doing so, they can also discriminate between individuals sharing …

Developing fair deep learning models for identity-sensitive applications (eg, face attribute
recognition) has gained increasing attention from the research community. Indeed, it has …

In this critical survey, we analyze typical claims on the relationship between explainable AI
(XAI) and fairness to disentangle the multidimensional relationship between these two …