SOURCE-LINKED INTELLIGENCE

Fairness in multi-class multi-group classification problems via contextial coherent risk measures

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

We propose a new design of fair classifiers for multi-class classification problems in the presence of vector-valued sensitive attributes. In that scenario each sensitive attribute has multiple values and forms several groups relevant to the fairness consideration. Naturally those groups are overlapping and one should also analyze the interaction of factors. Additionally, the decision makers aided by the classification should not violate individual rights at the expense of satisfying fairness metrics at the group level. We propose an approach using the theory and methods of coherent measures o

Read original source ↗ Open in workspace

recordType

paper

region

Global