  In recent years, the problem of addressing fairness in Machine Learning (ML)
and automatic decision-making has attracted a lot of attention in the
scientific communities dealing with Artificial Intelligence. A plethora of
different definitions of fairness in ML have been proposed, that consider
different notions of what is a "fair decision" in situations impacting
individuals in the population. The precise differences, implications and
"orthogonality" between these notions have not yet been fully analyzed in the
literature. In this work, we try to make some order out of this zoo of
definitions.
