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.
This document was originally published in Foundations and Trends® in Information Retrieval by Now Publishers inc. Copyright restrictions may apply. http://doi.org/10.1561/1500000079
Ekstrand, Michael D.; Das, Anubrata; Burke, Robin; and Diaz, Fernando. (2022). "Fairness in Information Access Systems". Foundations and Trends® in Information Retrieval, 16(1-2), 1-177. http://doi.org/10.1561/1500000079