  Social connections are a conduit through which individuals communicate,
information propagates, and diseases spread. Identifying individuals that are
more likely to adopt ideas or technologies and spread them to others is
essential in order to develop effective information campaigns, fight epidemics,
and to maximize the reach of limited resources. Consequently a lot of work has
focused on identifying sets of influencers. Here we show that seeding
information using these influence maximization methods, only benefits connected
and central individuals, consistently leaving the most vulnerable behind. Our
results highlights troublesome outcomes of influence maximization algorithms:
they do not disseminate information in an equitable manner threatening to
create an increasingly unequal society. To overcome this issue we devise a
simple, multi-objective algorithm, which maximises both influence and
information equity. Our work demonstrates how to find fairer influencer sets,
highlighting that in our search for maximizing information, we do not need to
compromise on information equality.
