Deep Learning for Face Recognition: Pride or Prejudiced?
It addresses bias in AI for fairer face recognition systems, though it is incremental as it applies existing methods to analyze bias rather than proposing new solutions.
This research investigates whether deep learning-based face recognition systems exhibit in-group biases similar to humans, such as own-race and own-age biases, by analyzing 36 experiments across multiple datasets using four deep networks trained on over 10 million images, finding that these networks encode race or age-specific features and mimic human behavior.
Do very high accuracies of deep networks suggest pride of effective AI or are deep networks prejudiced? Do they suffer from in-group biases (own-race-bias and own-age-bias), and mimic the human behavior? Is in-group specific information being encoded sub-consciously by the deep networks? This research attempts to answer these questions and presents an in-depth analysis of `bias' in deep learning based face recognition systems. This is the first work which decodes if and where bias is encoded for face recognition. Taking cues from cognitive studies, we inspect if deep networks are also affected by social in- and out-group effect. Networks are analyzed for own-race and own-age bias, both of which have been well established in human beings. The sub-conscious behavior of face recognition models is examined to understand if they encode race or age specific features for face recognition. Analysis is performed based on 36 experiments conducted on multiple datasets. Four deep learning networks either trained from scratch or pre-trained on over 10M images are used. Variations across class activation maps and feature visualizations provide novel insights into the functioning of deep learning systems, suggesting behavior similar to humans. It is our belief that a better understanding of state-of-the-art deep learning networks would enable researchers to address the given challenge of bias in AI, and develop fairer systems.