  In this paper, we argue for the adoption of a normative definition of
fairness within the machine learning community. After characterizing this
definition, we review the current literature of Fair ML in light of its
implications. We end by suggesting ways to incorporate a broader community and
generate further debate around how to decide what is fair in ML.
