Advances in computer science and data-analytic methods are driving a new era in mental
health research and application. Artificial intelligence (AI) technologies hold the potential to …

Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

Abstract Trustworthy Artificial Intelligence (AI) is based on seven technical requirements
sustained over three main pillars that should be met throughout the system's entire life cycle …

Systems incorporating biometric technologies have become ubiquitous in personal,
commercial, and governmental identity management applications. Both cooperative (eg …

Face recognition (FR) systems have a growing effect on critical decision-making processes.
Recent works have shown that FR solutions show strong performance differences based on …

We reveal critical insights into problems of bias in state-of-the-art facial recognition (FR)
systems using a novel Balanced Faces in the Wild (BFW) dataset: data balanced for gender …

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 …

Whilst face recognition applications are becoming increasingly prevalent within our daily
lives, leading approaches in the field still suffer from performance bias to the detriment of …

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 …

The presence of decision-making algorithms in society is rapidly increasing nowadays,
while concerns about their transparency and the possibility of these algorithms becoming …