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 …
K Karkkainen, J Joo - Proceedings of the IEEE/CVF winter …, 2021 - openaccess.thecvf.com
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 …
K Lloyd - arXiv preprint arXiv:1809.07842, 2018 - arxiv.org
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 …
T Gebru - arXiv preprint arXiv:1908.06165, 2019 - arxiv.org
From massive face-recognition-based surveillance and machine-learning-based decision systems predicting crime recidivism rates, to the move towards automated health diagnostic …