arXiv Open Access 2017

The Authority of "Fair" in Machine Learning

Michael Skirpan Micha Gorelick

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Abstrak

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.

Topik & Kata Kunci

cs.CY

Penulis (2)

Michael Skirpan

Micha Gorelick

Format Sitasi

APA MLA BibTeX

Skirpan, M., Gorelick, M. (2017). The Authority of "Fair" in Machine Learning. https://arxiv.org/abs/1706.09976

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Informasi Jurnal

Tahun Terbit: 2017
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓