Not So Fair: The Impact of Presumably Fair Machine Learning Models-Reference-Cited by-同舟云学术

Not So Fair: The Impact of Presumably Fair Machine Learning Models

Published:2023-08-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society
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Author:

Jorgensen Mackenzie¹^ORCID,Richert Hannah²^ORCID,Black Elizabeth¹^ORCID,Criado Natalia³^ORCID,Such Jose¹^ORCID

Affiliation:

1. King's College London, United Kingdom

2. Universität Osnabrück, Germany

3. Universitat Politecnica de Valencia, Spain

Funder

Engineering and Physical Sciences Research Council

German Academic Exchange Service

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3600211.3604699

Reference45 articles.

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2. Solon Barocas Moritz Hardt and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. http://www.fairmlbook.org. Solon Barocas Moritz Hardt and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. http://www.fairmlbook.org.

3. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

4. Ruha Benjamin . 2019. Race After Technology: Abolitionist Tools for the New Jim Code . Polity , Medford, MA . Ruha Benjamin. 2019. Race After Technology: Abolitionist Tools for the New Jim Code. Polity, Medford, MA.

5. Sarah Bird , Miroslav Dudík , Hanna Wallach , and Kathleen Walker . 2020 . Fairlearn: A toolkit for assessing and improving fairness in AI. Technical Report. Microsoft. 6 pages. Sarah Bird, Miroslav Dudík, Hanna Wallach, and Kathleen Walker. 2020. Fairlearn: A toolkit for assessing and improving fairness in AI. Technical Report. Microsoft. 6 pages.

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