The Authority of "Fair" in Machine Learning
arXiv:1706.09976
Abstract
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.
Presented as a talk at the 2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017)
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