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.

翻译：社交连接是个体间沟通、信息传播和疾病蔓延的渠道。识别更可能采纳观点或技术并将其传播给他人的个体，对于制定有效的信息宣传、抗击流行病以及最大化有限资源的覆盖范围至关重要。因此，大量研究聚焦于识别影响者群体。本文表明，采用这些影响力最大化方法进行信息播种，仅惠及具有连接性和中心性的个体，始终将最弱势群体排除在外。我们的结果揭示了影响力最大化算法的不良后果：它们无法以公平的方式传播信息，可能加剧社会不平等。为解决这一问题，我们设计了一种简单的多目标算法，该算法同时优化影响力和信息公平性。本研究展示了如何寻找更公平的影响者集合，并强调在追求信息最大化的过程中，我们无需在信息公平性上做出妥协。