Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …

Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

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 …

Clustering algorithms are widely used in many societal resource allocation applications,
such as loan approvals and candidate recruitment, among others, and hence, biased or …

Classification models are ubiquitously deployed in society and necessitate high utility,
fairness, and robustness performance. Current research efforts mainly focus on improving …

Optimizing prediction accuracy can come at the expense of fairness. Towards minimizing
discrimination against a group, fair machine learning algorithms strive to equalize the …

Given the increasing prevalence of facial analysis technology, the problem of bias in these
tools is becoming an even greater source of concern. Causality has been proposed as a …