  Biases inherent in both data and algorithms make the fairness of widespread
machine learning (ML)-based decision-making systems less than optimal. To
improve the trustfulness of such ML decision systems, it is crucial to be aware
of the inherent biases in these solutions and to make them more transparent to
the public and developers. In this work, we aim at providing a set of
explainability tool that analyse the difference in the face recognition models'
behaviors when processing different demographic groups. We do that by
leveraging higher-order statistical information based on activation maps to
build explainability tools that link the FR models' behavior differences to
certain facial regions. The experimental results on two datasets and two face
recognition models pointed out certain areas of the face where the FR models
react differently for certain demographic groups compared to reference groups.
The outcome of these analyses interestingly aligns well with the results of
studies that analyzed the anthropometric differences and the human judgment
differences on the faces of different demographic groups. This is thus the
first study that specifically tries to explain the biased behavior of FR models
on different demographic groups and link it directly to the spatial facial
features. The code is publicly available here.
