1. Fairness-Aware Estimation of Graphical Models
- Author
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Zhou, Zhuoping, Tarzanagh, Davoud Ataee, Hou, Bojian, Long, Qi, and Shen, Li
- Subjects
Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
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: 32 Pages, 9 Figures
- Published
- 2024