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Formal Group Fairness and Accuracy in Automated Decision Making

  • Most research on fairness in Machine Learning assumes the relationship between fairness and accuracy to be a trade-off, with an increase in fairness leading to an unavoidable loss of accuracy. In this study, several approaches for fair Machine Learning are studied to experimentally analyze the relationship between accuracy and group fairness. The results indicated that group fairness and accuracy may even benefit each other, which emphasizes the importance of selecting appropriate measures for performance evaluation. This work provides a foundation for further studies on the adequate objectives of Machine Learning in the context of fair automated decision making.

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Metadaten
Author:Anna Langenberg, Shih-Chi MaORCiD, Tatiana ErmakovaORCiD, Benjamin FabianORCiD
URN:urn:nbn:de:kobv:526-opus4-17323
DOI (Version of Record):https://doi.org/10.3390/math11081771
ISSN:2227-7390
Parent Title (English):Mathematics
Publisher:MDPI
Document Type:Article
Language:English
Year of Publication:2023
Publishing Institution:Technische Hochschule Wildau
Release Date:2023/04/17
Tag:AI; algorithmic bias; automated decision making; group fairness; machine learning; metric
Volume:11
Issue:8
Article Number:1771
Source:Langenberg, A.; Ma, S.-C.; Ermakova, T.; Fabian, B. Formal Group Fairness and Accuracy in Automated Decision Making. Mathematics 2023, 11, 1771. https://doi.org/10.3390/math11081771
Faculties an central facilities:Fachbereich Wirtschaft, Informatik, Recht
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
5 Naturwissenschaften und Mathematik / 51 Mathematik / 519 Wahrscheinlichkeiten, angewandte Mathematik
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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