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 …

Algorithmic bias and fairness in the context of graph mining have largely remained nascent.
The sparse literature on fair graph mining has almost exclusively focused on group-based …

In today's increasingly connected world, graph mining plays a pivotal role in many real-world
application domains, including social network analysis, recommendations, marketing and …

Nowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in
many real-world application domains where decisions can have a strong societal impact …

We consider fair graph representation learning via data augmentations. While this direction
has been explored previously, existing methods invariably rely on certain assumptions on …

Fair machine learning aims to mitigate the biases of model predictions against certain
subpopulations regarding sensitive attributes such as race and gender. Among the many …

Disparate impact has raised serious concerns in machine learning applications and its
societal impacts. In response to the need of mitigating discrimination, fairness has been …

Abstract Machine learning and data mining algorithms have been increasingly used recently
to support decision-making systems in many areas of high societal importance such as …

An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence (AI) and machine learning (ML) algorithms in spheres …

We study three classical machine learning algorithms in the context of algorithmic fairness:
adaptive boosting, support vector machines, and logistic regression. Our goal is to maintain …