Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks

arXiv:2405.02225v1 Announce Type: cross
Abstract: This paper introduces a framework for post-processing machine learning models so that their predictions satisfy multi-group fairness guarantees. Based on the celebrated notion of multicalibration, we introduce $(\mathbf{s},\mathcal{G}, \alpha)-$GMC (Generalized Multi-Dimensional Multicalibration) for multi-dimensional mappings $\mathbf{s}$, constraint set $\mathcal{G}$, and a pre-specified threshold level $\alpha$. We propose associated algorithms to achieve this notion in general settings. This framework is then applied to diverse scenarios encompassing different fairness concerns, including false negative rate control in image …

abstract alpha arxiv control cs.ai cs.cy cs.lg fair fairness framework generalized machine machine learning machine learning models notion paper post-processing predictions processing risk risks set stat.me stat.ml threshold type