Fairness in Information Access Systems

TL;DR

This paper surveys the emerging field of fairness in information access systems, highlighting unique challenges and proposing a taxonomy to categorize different fairness dimensions, aiming to guide future research.

Contribution

It provides the first comprehensive taxonomy and literature review of fairness in information access, addressing its unique challenges compared to traditional fair machine learning.

Findings

Identifies key dimensions of fair information access

Highlights challenges due to personalization and multistakeholder settings

Outlines open problems and research directions

Abstract

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.