The Authority of "Fair" in Machine Learning

Understand

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.