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Published, Version of Record (VoR)Open Access (License Unspecified), Open
Journal article
Fairness in Information Access Systems
Foundations and trends in information retrieval, v 16(1-2), pp 1-174
01 Jan 2022
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.
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Details
- Title
- Fairness in Information Access Systems
- Creators
- Michael D. Ekstrand - Boise State UniversityAnubrata Das - The University of Texas at AustinRobin Burke - University of Colorado BoulderFernando Diaz - Mila - Quebec Artificial Intelligence Institute
- Publication Details
- Foundations and trends in information retrieval, v 16(1-2), pp 1-174
- Publisher
- Now Publishers Inc
- Number of pages
- 174
- Grant note
- Good Systems Wipro IIS 19-11025; IIS 17-51278 / National Science Foundation; National Science Foundation (NSF) Micron Foundation
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000934132300001
- Scopus ID
- 2-s2.0-85134161155
- Other Identifier
- 991021818499104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems