TY - JOUR A1 - Langenberg, Anna A1 - Ma, Shih-Chi A1 - Ermakova, Tatiana A1 - Fabian, Benjamin T1 - Formal Group Fairness and Accuracy in Automated Decision Making TI - Mathematics N2 - 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. KW - AI KW - machine learning KW - automated decision making KW - algorithmic bias KW - metric KW - group fairness Y1 - 2023 UR - https://opus4.kobv.de/opus4-th-wildau/frontdoor/index/index/docId/1732 UR - https://nbn-resolving.org/urn:nbn:de:kobv:526-opus4-17323 SN - 2227-7390 VL - 11 IS - 8 PB - MDPI ER -