Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach-Reference-Cited by-同舟云学术

Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach

Published:2022 Issue: Volume: Page:117-129
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

d’Aloisio Giordano^ORCID,Stilo Giovanni^ORCID,Di Marco Antinisca^ORCID,D’Angelo Andrea^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-09316-6_11

Reference31 articles.

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5. Bird, S., et al.: Fairlearn: a toolkit for assessing and improving fairness in AI. Tech. Rep. MSR-TR-2020-32, Microsoft, May 2020. https://www.microsoft.com/en-us/research/publication/fairlearn-a-toolkit-for-assessing-and-improving-fairness-in-ai/

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