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

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 …

This work proposes a novel privacy-preserving neural network feature representation to
suppress the sensitive information of a learned space while maintaining the utility of the …

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 …

We propose a discrimination-aware learning method to improve both the accuracy and
fairness of biased face recognition algorithms. The most popular face recognition …

This work explores the biases in learning processes based on deep neural network
architectures. We analyze how bias affects deep learning processes through a toy example …

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

Digital medicine is already well established in respiratory medicine through remote
monitoring digital devices which are used in the day-to-day care of patients with asthma …

We introduce leave-one-out unfairness, which characterizes how likely a model's prediction
for an individual will change due to the inclusion or removal of a single other person in the …