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, 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 complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant, let alone measuring or promoting them. 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 use of this work by scholars with experience in one (or neither) of these fields who wish to learn about their intersection. We conclude with several open problems in fair information access, along with some suggestions for how to approach research in this space.
翻译:建议、信息检索和其他信息存取系统对调查和运用为研究其他机器学习系统而开发的公平和不歧视概念提出了独特的挑战。虽然公平的信息存取与公平分类有许多共同之处,但信息存取应用程序的多利益攸关方性质、基于等级的问题设置、个人化在许多情况下的中心地位以及用户反应的作用,使准确确定哪些公平类型和操作可能相关,更不用说衡量或促进公平概念的问题复杂化。在这份专著中,我们介绍了公平信息存取的各个方面的分类,并调查了迄今为止关于这一新和迅速发展的专题的文献。我们先简要介绍信息存取和算法公正,以便利在这些领域中一个(或两个)经验丰富的学者利用这项工作,他们希望了解它们的交叉性。我们最后提出了公平信息存取方面的几个公开问题,并就如何在这一空间进行研究提出了一些建议。