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 …

Fairness and robustness are critical elements of Trustworthy AI that need to be addressed
together. Fairness is about learning an unbiased model while robustness is about learning …

The concept of the Butterfly Effect, derived from chaos theory, highlights how seemingly
minor changes can lead to significant, unpredictable outcomes in complex systems. This …

The impact of automated decision-making systems on human lives is growing, emphasizing
the need for these systems to be not only accurate but also fair. The field of algorithmic …

Deep learning (DL) systems have been gaining popularity in critical tasks such as credit
evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL …

A wide variety of fairness metrics and eXplainable Artificial Intelligence (XAI) approaches
have been proposed in the literature to identify bias in machine learning models that are …

We study the problem of learning fair prediction models for unseen test sets distributed
differently from the train set. Stability against changes in data distribution is an important …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

Recent studies found that using machine learning for social applications can lead to
injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes. To …