Getfair: Generalized fairness tuning of classification models

DOI:	https://doi.org/10.1145/3531146.3533094
URL:	https://dl.acm.org/doi/10.1145/3531146.3533094
Document Type:	Conference or workshop publication
Year of publication:	2022
Book title:	Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2022) : June 21-24, 2022, Seoul, Korea
Page range:	289-299
Conference title:	FAccT '22, 5. Annual ACM FAccT Conference 2022
Location of the conference venue:	Seoul, South Korea
Date of the conference:	21.-24.06.2022
Publisher:	Isbell, Charles ; Lazar, Seth ; Oh, Alice ; Xiang, Alice
Place of publication:	New York, NY
Publishing house:	ACM
ISBN:	978-1-4503-9352-2
Publication language:	English
Institution:	Business School > Data Science in the Economic and Social Sciences (Strohmaier, 2022-)
Subject:	000 Generalities
Keywords (English):	fair classification , fairness metrics , sensitive attribute , classifier models

Dieser Eintrag ist Teil der Universitätsbibliographie.

BASE: Sikdar, Sandipan ; Lemmerich, Florian ; Strohmaier, Markus

Google Scholar: Sikdar, Sandipan ; Lemmerich, Florian ; Strohmaier, Markus

ORCID: Sikdar, Sandipan, Lemmerich, Florian and Strohmaier, Markus

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