  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.
