Algorithmic fairness (AF) has been framed as a newly emerging technology that mitigates
systemic discrimination in automated decision‐making, providing opportunities to improve …

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or
Machine Learning (ML) field is undergoing rapid growth concerning research and real-world …

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 …

We study the problem of learning classifiers that perform well across (known or unknown)
groups of data. After observing that common worst-group-accuracy datasets suffer from …

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 …

Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications.
Despite the successes of GCN deployment, GCN often exhibits performance disparity with …

Price discrimination strategies, which offer different prices to customers based on differences
in their valuations, have become common practice. Although it allows sellers to increase …

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 …

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 …

This paper provides a mathematical optimization framework to incorporate fairness
measures from the facilities' perspective to discrete and continuous maximal covering …