@inproceedings{,
    author = {Roy, Arjun; Iosifidis, Vasileios; Ntoutsi, Eirini},
    title = {Multi-fairness Under Class-Imbalance},
    editor = {Pascal, Poncelet; Ienco, Dino},
    booktitle = {Discovery Science : 25th International Conference, DS 2022, Montpellier, France, October 10–12, 2022, Proceedings},
    series = {Lecture Notes in Computer Science},
    journal = {},
    address = {Cham},
    publisher = {Springer},
    edition = {},
    year = {2022},
    isbn = {978-3-031-18840-4 ; 978-3-031-18839-8},
    volume = {13601},
    number =  {},
    pages = {286-301},
    url = {https://doi.org/10.1007/978-3-031-18840-4_21},
    doi = {10.1007/978-3-031-18840-4_21},
    keywords = {},
    abstract = {Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced. The class-imbalance problem is more severe for the protected group in the critical minority class (e.g., female +, non-white +, etc.). Still, existing methods focus only on the overall error-discrimination trade-off, ignoring the imbalance problem, and thus they amplify the prevalent bias in the minority classes. To solve the combined problem of multi-discrimination and class-imbalance we introduce a new fairness measure, Multi-Max Mistreatment (MMM), which considers both (multi-attribute) protected group and class membership of instances to measure discrimination. To solve the combined problem, we propose Multi-Fair Boosting Post Pareto (MFBPP) a boosting approach that incorporates MMM-costs in the distribution update and post-training, selects the optimal trade-off among accurate, class-balanced, and fair solutions. The experimental results show the superiority of our approach against state-of-the-art methods in producing the best balanced performance across groups and classes and the best accuracy for the protected groups in the minority class.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Informatik, INF 7 - Institut für Datensicherheit, Professur: Ntoutsi, Eirini},
    
    
}