Tag: protected

Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation arXiv:2509.08163v1 Announce Type: cross Abstract: Ensuring equitable treatment (fairness) across protected attributes (such as gender or ethnicity) is a critical issue in machine learning. Most existing literature focuses on binary classification, but achieving fairness in regression tasks-such as insurance pricing or hiring score assessments-is equally…