Recent research has highlighted the vulnerabilities of modern machine learning based
systems to bias, especially towards segments of society that are under-represented in …

Fairness has become an important agenda in computer vision and artificial intelligence.
Recent studies have shown that many computer vision models and datasets exhibit …

Measuring algorithmic bias is crucial both to assess algorithmic fairness and to guide the
improvement of algorithms. Current bias measurement methods in computer vision are …

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 …

Existing public face image datasets are strongly biased toward Caucasian faces, and other
races (eg, Latino) are significantly underrepresented. The models trained from such datasets …

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

As Artificial Intelligence (AI) technologies proliferate, concern has centered around the long-
term dangers of job loss or threats of machines causing harm to humans. All of this concern …

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 …

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 …

From massive face-recognition-based surveillance and machine-learning-based decision
systems predicting crime recidivism rates, to the move towards automated health diagnostic …