  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.
