Fairness-Aware Estimation of Graphical Models
<|reference_start|>Fairness-Aware Estimation of Graphical Models: 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.<|reference_end|>
@article{zhou2024fairness-aware,
    title={Fairness-Aware Estimation of Graphical Models},
    author={Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Qi Long, Li Shen},
    journal={arXiv preprint arXiv:2408.17396},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17396},
    primaryClass={cs.LG stat.ML}
}