  This work explores the biases in learning processes based on deep neural
network architectures. We analyze how bias affects deep learning processes
through a toy example using the MNIST database and a case study in gender
detection from face images. We employ two gender detection models based on
popular deep neural networks. We present a comprehensive analysis of bias
effects when using an unbalanced training dataset on the features learned by
the models. We show how bias impacts in the activations of gender detection
models based on face images. We finally propose InsideBias, a novel method to
detect biased models. InsideBias is based on how the models represent the
information instead of how they perform, which is the normal practice in other
existing methods for bias detection. Our strategy with InsideBias allows to
detect biased models with very few samples (only 15 images in our case study).
Our experiments include 72K face images from 24K identities and 3 ethnic
groups.
