  Rapid development of artificial intelligence (AI) systems amplify many
concerns in society. These AI algorithms inherit different biases from humans
due to mysterious operational flow and because of that it is becoming adverse
in usage. As a result, researchers have started to address the issue by
investigating deeper in the direction towards Responsible and Explainable AI.
Among variety of applications of AI, facial expression recognition might not be
the most important one, yet is considered as a valuable part of human-AI
interaction. Evolution of facial expression recognition from the feature based
methods to deep learning drastically improve quality of such algorithms. This
research work aims to study a gender bias in deep learning methods for facial
expression recognition by investigating six distinct neural networks, training
them, and further analysed on the presence of bias, according to the three
definition of fairness. The main outcomes show which models are gender biased,
which are not and how gender of subject affects its emotion recognition. More
biased neural networks show bigger accuracy gap in emotion recognition between
male and female test sets. Furthermore, this trend keeps for true positive and
false positive rates. In addition, due to the nature of the research, we can
observe which types of emotions are better classified for men and which for
women. Since the topic of biases in facial expression recognition is not well
studied, a spectrum of continuation of this research is truly extensive, and
may comprise detail analysis of state-of-the-art methods, as well as targeting
other biases.

Computer Vision and Pattern Recognition