Enforcing Fair Predicted Scores on Intervals of Percentiles by Difference-of-Convex Constraints

ArXi:2505.12530v2 Announce Type: replace Fairness in machine learning has become a critical concern. Existing approaches often focus on achieving full fairness across all score ranges generated by predictive models, ensuring fairness in both high- and low-percentile populations. However, this stringent requirement can compromise predictive performance and may not align with the practical fairness concerns of stakeholders.