Fairness-Aware Estimation of Graphical Models

TL;DR

This paper introduces a novel framework for fair estimation of graphical models that reduces bias related to sensitive attributes while preserving model accuracy, validated through experiments on synthetic and real data.

Contribution

It proposes a comprehensive, multi-objective optimization approach integrating disparity error and custom loss to enhance fairness in graphical model estimation.

Findings

Effectively mitigates bias in GMs across sensitive groups

Maintains high accuracy in model estimation

Demonstrates robustness on real-world datasets

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.