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 …

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 …

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 …

Training datasets for machine learning often have some form of missingness. For example,
to learn a model for deciding whom to give a loan, the available training data includes …

Algorithmic fairness has aroused considerable interests in data mining and machine
learning communities recently. So far the existing research has been mostly focusing on the …

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 …

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

We initiate the study of fair classifiers that are robust to perturbations in the training
distribution. Despite recent progress, the literature on fairness has largely ignored the …

We consider a fair representation learning perspective, where optimal predictors, on top of
the data representation, are ensured to be invariant with respect to different sub-groups …

Analysis of the fairness of machine learning (ML) algorithms has attracted many researchers'
interest. Several studies have shown that ML methods produce a bias toward different …