  Over the past decades the machine and deep learning community has celebrated
great achievements in challenging tasks such as image classification. The deep
architecture of artificial neural networks together with the plenitude of
available data makes it possible to describe highly complex relations. Yet, it
is still impossible to fully capture what the deep learning model has learned
and to verify that it operates fairly and without creating bias, especially in
critical tasks, for instance those arising in the medical field. One example
for such a task is the detection of distinct facial expressions, called Action
Units, in facial images. Considering this specific task, our research aims to
provide transparency regarding bias, specifically in relation to gender and
skin color. We train a neural network for Action Unit classification and
analyze its performance quantitatively based on its accuracy and qualitatively
based on heatmaps. A structured review of our results indicates that we are
able to detect bias. Even though we cannot conclude from our results that lower
classification performance emerged solely from gender and skin color bias,
these biases must be addressed, which is why we end by giving suggestions on
how the detected bias can be avoided.
