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Multi-fairness under class-imbalance
[article]
2022
arXiv
pre-print
Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced. The class-imbalance problem is more severe for the often underrepresented protected group (e.g. female, non-white, etc.) in the critical minority class. Still, existing methods focus only on the overall error-discrimination trade-off, ignoring the imbalance problem, thus amplifying the prevalent bias in the minority
arXiv:2104.13312v3
fatcat:gu6jq2g7avbutlpns75qdijfia