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Formal Group Fairness and Accuracy in Automated Decision Making

Author

Listed:
  • Anna Langenberg

    (Information Systems, Humboldt-Universität zu Berlin, 10178 Berlin, Germany)

  • Shih-Chi Ma

    (Information Systems, Humboldt-Universität zu Berlin, 10178 Berlin, Germany
    EDIH pro_digital, Technical University of Applied Sciences Wildau, 15745 Wildau, 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
    EDIH pro_digital, Technical University of Applied Sciences Wildau, 15745 Wildau, Germany)

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.

Suggested Citation

  • Anna Langenberg & Shih-Chi Ma & Tatiana Ermakova & Benjamin Fabian, 2023. "Formal Group Fairness and Accuracy in Automated Decision Making," Mathematics, MDPI, vol. 11(8), pages 1-25, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1771-:d:1118427
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    References listed on IDEAS

    as
    1. Nikita Kozodoi & Johannes Jacob & Stefan Lessmann, 2021. "Fairness in Credit Scoring: Assessment, Implementation and Profit Implications," Papers 2103.01907, arXiv.org, revised Jun 2022.
    2. Richard Berk & Hoda Heidari & Shahin Jabbari & Michael Kearns & Aaron Roth, 2021. "Fairness in Criminal Justice Risk Assessments: The State of the Art," Sociological Methods & Research, , vol. 50(1), pages 3-44, February.
    3. Kozodoi, Nikita & Jacob, Johannes & Lessmann, Stefan, 2022. "Fairness in credit scoring: Assessment, implementation and profit implications," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1083-1094.
    Full references (including those not matched with items on IDEAS)

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