Fairness-Aware Estimation of Graphical Models

Autor:	Zhou, Zhuoping, Tarzanagh, Davoud Ataee, Hou, Bojian, Long, Qi, Shen, Li
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This paper examines the issue of fairness in the estimation of graphical models (GMs), particularly Gaussian, Covariance, and Ising models. These models play a vital role in understanding complex relationships in high-dimensional data. However, standard GMs can result in biased outcomes, especially when the underlying data involves sensitive characteristics or protected groups. To address this, we introduce a comprehensive framework designed to reduce bias in the estimation of GMs related to protected attributes. Our approach involves the integration of the pairwise graph disparity error and a tailored loss function into a nonsmooth multi-objective optimization problem, striving to achieve fairness across different sensitive groups while maintaining the effectiveness of the GMs. Experimental evaluations on synthetic and real-world datasets demonstrate that our framework effectively mitigates bias without undermining GMs' performance. Comment: Accepted for publication at NeurIPS 2024, 34 Pages, 9 Figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.17396 Zobrazit plný text záznamu View this record from Arxiv