Foundations and Trends® in Information Retrieval > Vol 16 > Issue 1-2

Fairness in Information Access Systems

By Michael D. Ekstrand, People and Information Research Team (PIReT), Boise State University, USA, ekstrand@acm.org | Anubrata Das, School of Information, University of Texas at Austin, USA, anubrata@utexas.edu | Robin Burke, Department of Information Science, University of Colorado, USA, robin.burke@colorado.edu | Fernando Diaz, Mila - Quebec AI Institute, Canada, diazf@acm.org

 
Suggested Citation
Michael D. Ekstrand, Anubrata Das, Robin Burke and Fernando Diaz (2022), "Fairness in Information Access Systems", Foundations and TrendsĀ® in Information Retrieval: Vol. 16: No. 1-2, pp 1-177. http://dx.doi.org/10.1561/1500000079

Publication Date: 11 Jul 2022
© 2022 M. D. Ekstrand et al.
 
Subjects
Information Retrieval
 

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In this article:
1. Introduction
2. Information Access Fundamentals
3. Fairness Fundamentals
4. The Problem Space
5. Consumer Fairness
6. Provider Fairness
7. Dynamic Fairness
8. Next Steps for Fair Information Access
Acknowledgements
Appendix
References
Index

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, 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.

DOI:10.1561/1500000079
ISBN: 978-1-63828-040-8
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Table of contents:
1. Introduction
2. Information Access Fundamentals
3. Fairness Fundamentals
4. The Problem Space
5. Consumer Fairness
6. Provider Fairness
7. Dynamic Fairness
8. Next Steps for Fair Information Access
Acknowledgements
Appendix
References
Index

Fairness in Information Access Systems

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 such as 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. These all complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant.

In this monograph, the authors present a taxonomy of the various dimensions of fair information access and survey the literature to date on this new and rapidly-growing topic. They preface this with brief introductions to information access and algorithmic fairness to facilitate the use of this work by scholars who wish to study their intersection. The authors conclude with several open problems in fair information access and present suggestions for how to approach research in this space.

 
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