Fairness-Aware Estimation of Graphical Models
This addresses fairness issues in graphical models for data analysis involving protected groups, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of bias in graphical models (Gaussian, Covariance, and Ising models) due to sensitive attributes by introducing a fairness-aware framework, which reduced bias while maintaining model performance as shown in experiments on synthetic and real-world datasets.
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.