research-article

When Fair Ranking Meets Uncertain Inference

Authors:

Christo WilsonAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1033 - 1043

https://doi.org/10.1145/3404835.3462850

Published: 11 July 2021 Publication History

Abstract

Existing fair ranking systems, especially those designed to be demographically fair, assume that accurate demographic information about individuals is available to the ranking algorithm. In practice, however, this assumption may not hold --- in real-world contexts like ranking job applicants or credit seekers, social and legal barriers may prevent algorithm operators from collecting peoples' demographic information. In these cases, algorithm operators may attempt to infer peoples' demographics and then supply these inferences as inputs to the ranking algorithm.

In this study, we investigate how uncertainty and errors in demographic inference impact the fairness offered by fair ranking algorithms. Using simulations and three case studies with real datasets, we show how demographic inferences drawn from real systems can lead to unfair rankings. Our results suggest that developers should not use inferred demographic data as input to fair ranking algorithms, unless the inferences are extremely accurate.

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Cited By

Schumacher TLutz MSikdar SStrohmaier M(2024)Properties of Group Fairness Measures for RankingsACM Transactions on Social Computing10.1145/3674883Online publication date: 27-Aug-2024
https://dl.acm.org/doi/10.1145/3674883
Cachel KRundensteiner ESerra ESpezzano F(2024)Wise Fusion: Group Fairness Enhanced Rank FusionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679649(163-174)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679649
Dodevska ZRadovanović SPetrović ADelibašić B(2023)When Fairness Meets Consistency in AHP Pairwise ComparisonsMathematics10.3390/math1103060411:3(604)Online publication date: 25-Jan-2023
https://doi.org/10.3390/math11030604
Show More Cited By

Index Terms

When Fair Ranking Meets Uncertain Inference
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
2. Social and professional topics
  1. Professional topics
    1. Computing profession
      1. Codes of ethics

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cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

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Published: 11 July 2021

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SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

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Cited By

Schumacher TLutz MSikdar SStrohmaier M(2024)Properties of Group Fairness Measures for RankingsACM Transactions on Social Computing10.1145/3674883Online publication date: 27-Aug-2024
https://dl.acm.org/doi/10.1145/3674883
Cachel KRundensteiner ESerra ESpezzano F(2024)Wise Fusion: Group Fairness Enhanced Rank FusionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679649(163-174)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679649
Dodevska ZRadovanović SPetrović ADelibašić B(2023)When Fairness Meets Consistency in AHP Pairwise ComparisonsMathematics10.3390/math1103060411:3(604)Online publication date: 25-Jan-2023
https://doi.org/10.3390/math11030604
Gorantla SMehrotra ADeshpande ALouis A(2023)Sampling Individually-Fair Rankings that are Always Group FairProceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3600211.3604671(205-216)Online publication date: 8-Aug-2023
https://dl.acm.org/doi/10.1145/3600211.3604671
Sakai TKim JKang I(2023)A Versatile Framework for Evaluating Ranked Lists in Terms of Group Fairness and RelevanceACM Transactions on Information Systems10.1145/358976342:1(1-36)Online publication date: 18-Aug-2023
https://dl.acm.org/doi/10.1145/3589763
Pinney CRaj AHanna AEkstrand M(2023)Much Ado About GenderProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578316(269-279)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578316
Fabris ASilvello GSusto GBiega AChua TLauw HSi LTerzi ETsaparas P(2023)Pairwise Fairness in Ranking as a Dissatisfaction MeasureProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570459(931-939)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570459
Liu FRahwan TAlShebli B(2023)Non-White scientists appear on fewer editorial boards, spend more time under review, and receive fewer citationsProceedings of the National Academy of Sciences10.1073/pnas.2215324120120:13Online publication date: 20-Mar-2023
https://doi.org/10.1073/pnas.2215324120
Amigó EDeldjoo YMizzaro SBellogín A(2023)A unifying and general account of fairness measurement in recommender systemsInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10311560:1Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.ipm.2022.103115
Deldjoo YJannach DBellogin ADifonzo AZanzonelli D(2023)Fairness in recommender systems: research landscape and future directionsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09364-z34:1(59-108)Online publication date: 24-Apr-2023
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