What's in a Name? Reducing Bias in Bios without Access to Protected Attributes

arXiv: Learning, 2019.

Cited by: 15|Bibtex|Views108
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race, gender, or age. However, this raises two significant challenges: (1) protected attributes may not be available or it may not be legal to use them, and (2) it is...More

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