@inproceedings{
zhou2024fairnessaware,
title={Fairness-Aware Estimation of Graphical Models},
author={Zhuoping Zhou and Davoud Ataee Tarzanagh and Bojian Hou and Qi Long and Li Shen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=WvWS8goWyR}
}
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.
Fairness-Aware Estimation of Graphical Models
Zhuoping Zhou Davoud Ataee Tarzanagh Bojian Hou Qi Long Li Shen