Exploring Algorithmic Fairness in Robust Graph Covering Problems

TL;DR

This paper addresses fairness issues in robust graph covering problems, proposing a new formulation with group fairness constraints, an approximation scheme, and demonstrating improved fairness and competitive coverage on real social networks.

Contribution

It introduces a novel formulation of robust graph covering with group fairness constraints and provides a tractable approximation scheme for real-world applications.

Findings

State-of-the-art algorithms exhibit biased node coverage without fairness constraints.

The proposed method significantly improves group fairness in node coverage.

The approach maintains competitive coverage while enhancing fairness on real social networks.

Abstract

Fueled by algorithmic advances, AI algorithms are increasingly being deployed in settings subject to unanticipated challenges with complex social effects. Motivated by real-world deployment of AI driven, social-network based suicide prevention and landslide risk management interventions, this paper focuses on robust graph covering problems subject to group fairness constraints. We show that, in the absence of fairness constraints, state-of-the-art algorithms for the robust graph covering problem result in biased node coverage: they tend to discriminate individuals (nodes) based on membership in traditionally marginalized groups. To mitigate this issue, we propose a novel formulation of the robust graph covering problem with group fairness constraints and a tractable approximation scheme applicable to real-world instances. We provide a formal analysis of the price of group fairness (PoF) for this problem, where we show that uncertainty can lead to greater PoF. We demonstrate the effectiveness of our approach on several real-world social networks. Our method yields competitive node coverage while significantly improving group fairness relative to state-of-the-art methods.