@conference {1499822,
	title = {Group-Fairness in Influence Maximization },
	booktitle = {International Joint Conference on Artificial Intelligence (IJCAI) 2019},
	year = {2019},
	abstract = {Influence maximization is a widely used model for
information dissemination in social networks. Recent work has employed such interventions across
a wide range of social problems, spanning public
health, substance abuse, and international development (to name a few examples). A critical but understudied question is whether the benefits of such
interventions are fairly distributed across different
groups in the population; e.g., avoiding discrimination with respect to sensitive attributes such as race
or gender. Drawing on legal and game-theoretic
concepts, we introduce formal definitions of fairness in influence maximization. We provide an algorithmic framework to find solutions which satisfy fairness constraints, and in the process improve
the state of the art for general multi-objective submodular maximization problems. Experimental results on real data from an HIV prevention intervention for homeless youth show that standard influence maximization techniques oftentimes neglect
smaller groups which contribute less to overall utility, resulting in a disparity which our proposed algorithms substantially reduce.},
	author = {Alan Tsang and Bryan Wilder and Eric Rice and Tambe, Milind and Yair Zick}
}
