Group fairness is a popular approach to prevent unfavorable treatment of individuals based on sensitive attributes such as race, gender, and disability. However, the reliance of group fairness on access to discrete group information raises several limitations and concerns, especially with regard to privacy, intersectionality, and unforeseen biases. In this work, we propose a "group-free" measure of fairness that does not rely on sensitive attributes and, instead, is based on homophily in social networks, i.e., the common property that individuals sharing similar attributes are more likely to be connected. Our measure is group-free as it avoids recovering any form of group memberships and uses only pairwise similarities between individuals to define inequality in outcomes relative to the homophily structure in the network. We theoretically justify our measure by showing it is commensurate with the notion of additive decomposability in the economic inequality literature and also bound the impact of non-sensitive confounding attributes. Furthermore, we apply our measure to develop fair algorithms for classification, maximizing information access, and recommender systems. Our experimental results show that the proposed approach can reduce inequality among protected classes without knowledge of sensitive attribute labels. We conclude with a discussion of the limitations of our approach when applied in real-world settings.

翻译：群体公平是一种旨在防止基于种族、性别、残疾等敏感属性对个人进行不公正对待的流行方法。然而，群体公平依赖于对离散群体信息的访问，这引发了一些限制和担忧，尤其是在隐私、交叉性和意外偏见方面。在这项工作中，我们提出了一种“无群体”的公平性度量方法，该方法不依赖于敏感属性，而是基于社交网络中的同质性，即具有相似属性的个体更有可能相互连接的常见特性。我们的度量是“无群体”的，因为它避免恢复任何形式的群体成员身份，仅利用个体之间的成对相似性来定义相对于网络同质性结构的结果不平等性。我们从理论上证明了我们的度量方法的合理性，表明它与经济不平等文献中的可加可分解性概念相称，并且限制了非敏感混淆属性的影响。此外，我们将我们的度量应用于开发分类、信息访问最大化和推荐系统中的公平算法。实验结果表明，所提出的方法可以在不了解敏感属性标签的情况下减少受保护类别之间的不平等性。最后，我们讨论了该方法在现实场景中应用的局限性。