  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.
