Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

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 …

The role of data in building AI systems has recently been significantly magnified by the
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …

Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …

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 …

As graph neural networks (GNNs) struggle with large-scale graphs due to high
computational demands, data distillation for graph data promises to alleviate this issue by …

A plethora of fair graph neural networks (GNNs) have been proposed to promote fairness in
models for high-stake real-life contexts. Meanwhile, explainability is generally proposed to …

A variety of fairness constraints have been proposed in the literature to mitigate group-level
statistical bias. Their impacts have been largely evaluated for different groups of populations …

Graph Neural Networks (GNNs) have demonstrated a great representation learning
capability on graph data and have been utilized in various downstream applications …

While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias
often gets reduced to enforcing an arbitrarily chosen fairness metric, either by enforcing …