Deep Learning for Face Recognition: Pride or Prejudiced?

TL;DR

This paper investigates whether deep learning face recognition systems encode social biases like race and age, analyzing their subconscious encoding and comparing their behavior to human in-group biases through extensive experiments.

Contribution

It is the first comprehensive study decoding where and how bias is encoded in deep face recognition networks, linking AI bias to human social biases with detailed visual and activation analyses.

Findings

Deep networks exhibit biases similar to humans in race and age recognition.

Visualizations reveal subconscious encoding of race and age features.

Analysis suggests deep networks mirror human social biases.

Abstract

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.