Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

Data-driven algorithms are only as good as the data they work with, while datasets,
especially social data, often fail to represent minorities adequately. Representation Bias in …

Research in machine learning fairness has historically considered a single binary
demographic attribute; however, the reality is of course far more complicated. In this work …

Federated learning has emerged as an important distributed learning paradigm, where a
server aggregates a global model from many client-trained models, while having no access …

The great advancements of generative adversarial networks and face recognition models in
computer vision have made it possible to swap identities on images from single sources …

This is the interim publication of the first International Scientific Report on the Safety of
Advanced AI. The report synthesises the scientific understanding of general-purpose AI--AI …

We introduce the psychometric concepts of bias and fairness in a multimodal machine
learning context assessing individuals' hireability from prerecorded video interviews. We …

Fair face recognition is all about learning invariant feature that generalizes to unseen faces
in any demographic group. Unfortunately, face datasets inevitably capture the imbalanced …

Demographic bias is a significant challenge in practical face recognition systems. Several
methods have been proposed to reduce the bias, which rely on accurate demographic …

Deep learning has catalysed progress in tasks such as face recognition and analysis,
leading to a quick integration of technological solutions in multiple layers of our society …