  Despite the high interest for Machine Learning (ML) in academia and industry,
many issues related to the application of ML to real-life problems are yet to
be addressed. Here we put forward one limitation which arises from a lack of
adaptation of ML models and datasets to specific applications. We formalise a
new notion of unfairness as exclusion of opinions. We propose ways to quantify
this unfairness, and aid understanding its causes through visualisation. These
insights into the functioning of ML-based systems hint at methods to mitigate
unfairness.
