Mitigating Bias in Facial Analysis Systems by Incorporating Label Diversity. (arXiv:2204.06364v2 [cs.CV] UPDATED)

Facial analysis models are increasingly applied in real-world applications
that have significant impact on peoples' lives. However, as literature has
shown, models that automatically classify facial attributes might exhibit
algorithmic discrimination behavior with respect to protected groups,
potentially posing negative impacts on individuals and society. It is therefore
critical to develop techniques that can mitigate unintended biases in facial
classifiers. Hence, in this work, we introduce a novel learning method that
combines both subjective human-based labels and objective annotations based …

analysis arxiv bias cv diversity facial analysis systems