Multi-Group Fairness Measures for Classification
: This paper proposes a robust, reliable, and reproducible methodology for evaluating group fairness in classification algorithms. Building upon established theoretical definitions of non-discrimination criteria - Independence, Separation, and Sufficiency-we present a comprehensive approach to quantifying fairness. Notably, our methodology extends beyond binary group comparisons to accommodate scenarios with multiple sensitive groups. Furthermore, we provide a methodological approach for assessing fairness within the Sufficiency criterion by operationalizing Calibration, elucidating critical issues and conceptual subtleties that appear to have been overlooked in existing literature. We assess the reliability and robustness of our model through applications to one real dataset.