Mitigating Bias in Facial Analysis Systems by Incorporating Label Diversity. (arXiv:2204.06364v1 [cs.CV])

Facial analysis models are increasingly applied in real-world applications
that have significant impact on peoples' lives. However, as previously 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 on …

analysis arxiv bias cv diversity facial analysis systems