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

Autor:	Zhang, Lujing, Roth, Aaron, Zhang, Linjun
Rok vydání:	2024
Předmět:	Statistics - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computers and Society Computer Science - Machine Learning Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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 segmentation, prediction set conditional uncertainty quantification in hierarchical classification, and de-biased text generation in language models. We conduct numerical studies on several datasets and tasks. Comment: 28 pages, 8 figures, accepted by ICML2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.02225 Zobrazit plný text záznamu View this record from Arxiv