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Fairness and Discrimination in Retrieval and Recommendation
Conference proceeding   Open access

Fairness and Discrimination in Retrieval and Recommendation

Michael D. Ekstrand, Robin Burke, Fernando Diaz and ACM
PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), pp 1403-1404
01 Jan 2019
DOI: https://doi.org/10.1145/3331184.3331380
url
https://doi.org/10.1145/3331184.3331380View
Published, Version of Record (VoR) Open

Abstract

Computer Science Computer Science, Information Systems Information Science & Library Science Science & Technology Technology
Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to information retrieval and related problems such as recommendation, as evidenced by the growing literature in SIGIR, FAT*, RecSys, and special sessions such as the FATREC workshop and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings into information retrieval and recommendation scenarios is not a straightforward task. This tutorial will help to orient IR researchers to algorithmic fairness, understand how concepts do and do not translate from other settings, and provide an introduction to the growing literature on this topic.

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26 citations in Web of Science
41 citations in Scopus

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Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Information Systems
Information Science & Library Science
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