Fairness in multi-class multi-group classification problems via contextial coherent risk measures
This work proposes a new design of fair classifiers for multi-class classification problems in the presence of vector-valued sensitive attributes and proposes a specialized numerical method for solving the resulting optimization problem.
Darinka Dentcheva, Xiangyu Tian
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