Formal Group Fairness and Accuracy in Automated Decision Making

Anna Langenberg, Shih-Chi Ma, Tatiana Ermakova and Benjamin Fabian ()
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Anna Langenberg: Information Systems, Humboldt-Universität zu Berlin, 10178 Berlin, Germany
Shih-Chi Ma: Information Systems, Humboldt-Universität zu Berlin, 10178 Berlin, Germany
Tatiana Ermakova: School of Computing, Communication and Business, Hochschule für Technik und Wirtschaft, University of Applied Sciences for Engineering and Economics, 10318 Berlin, Germany
Benjamin Fabian: Information Systems, Humboldt-Universität zu Berlin, 10178 Berlin, Germany

Mathematics, 2023, vol. 11, issue 8, 1-25

Abstract: 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.

Keywords: AI; machine learning; automated decision making; algorithmic bias; metrics; group fairness (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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