Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems. While fair information access shares many commonalities with fair classification, there are important differences: the multistakeholder nature of information access applications, the rank-based problem setting, the centrality of personalization in many cases, and the role of user response all complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant.

In this monograph, we present a taxonomy of the various dimensions of fair information access and survey the literature to date on this new and rapidly-growing topic. We preface this with brief introductions to information access and algorithmic fairness to facilitate the use of this work by scholars with experience in one (or neither) of these fields who wish to study their intersection. We conclude with several open problems in fair information access, along with some suggestions for how to approach research in this space.

推荐、信息检索和其他信息访问系统对调查和应用为研究其他机器学习系统而开发的公平和非歧视概念提出了独特的挑战。虽然公平信息访问与公平分类有许多共同点，但也有重要的区别：信息访问应用程序的多利益相关者性质、基于等级的问题设置、在许多情况下个性化的中心性以及用户响应的作用都使问题变得复杂化。准确识别可能相关的公平类型和可操作性。

在这本专着中，我们对公平信息访问的各个方面进行了分类，并调查了迄今为止关于这个新的和快速发展的话题的文献。我们在此之前简要介绍了信息访问和算法公平性，以方便在这些领域中的一个（或两者都没有）经验的希望研究其交叉点的学者使用这项工作。我们总结了公平信息访问中的几个未解决问题，以及有关如何在该领域进行研究的一些建议。