The Authority of "Fair" in Machine Learning

Michael Skirpan

Michael Skirpan and Micha Gorelick

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.

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