  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.
