Fairness in Information Access Systems
This work tackles fairness issues in information access systems, which is an incremental contribution by synthesizing existing knowledge for scholars at the intersection of information retrieval and algorithmic fairness.
The paper addresses the unique challenges of applying fairness concepts to recommendation and information retrieval systems, highlighting the complexities due to multistakeholder settings, ranking, personalization, and user interactions. It provides a taxonomy and survey of fair information access literature to guide researchers in this emerging field.
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.