The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

The explosion of data-driven applications using Artificial Intelligence (AI) in recent years has
given rise to a variety of ethical issues regarding data collection, annotation, and processing …

The use of machine learning to guide clinical decision making has the potential to worsen
existing health disparities. Several recent works frame the problem as that of algorithmic …

Algorithm fairness is an established line of research in the machine learning domain with
substantial work while the equivalent in the recommender system domain is relatively new …

We study fair classification in the presence of an omniscient adversary that, given an $\eta $,
is allowed to choose an arbitrary $\eta $-fraction of the training samples and arbitrarily …

Deep neural networks (DNNs) have demonstrated their outperformance in various domains.
However, it raises a social concern whether DNNs can produce reliable and fair decisions …

The past few years have seen a dramatic rise of academic and societal interest in fair
machine learning. While plenty of fair algorithms have been proposed recently to tackle this …

Machine learning (ML) components are increasingly incorporated into software products, yet
developers face challenges in transitioning from ML prototypes to products. Academic …

This article discusses transparency obligations introduced in the Artificial Intelligence Act,
the recently proposed European regulatory framework for artificial intelligence (AI). An …

With the increasing influence of machine learning algorithms in decision-making processes,
concerns about fairness have gained significant attention. This area now offers significant …