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On the Orthogonality of Bias and Utility in Ad hoc Retrieval

Authors:

Negar Arabzadeh,

Shirin Seyedsalehi,

Morteza Zihayat,

Ebrahim BagheriAuthors Info & Claims

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

Pages 1748 - 1752

https://doi.org/10.1145/3404835.3463110

Published: 11 July 2021 Publication History

Abstract

Various researchers have recently explored the impact of different types of biases on information retrieval tasks such as ad hoc retrieval and question answering. While the impact of bias needs to be controlled in order to avoid increased prejudices, the literature has often viewed the relationship between increased retrieval utility (effectiveness) and reduced bias as a tradeoff where one can suffer from the other. In this paper, we empirically study this tradeoff and explore whether it would be possible to reduce bias while maintaining similar retrieval utility. We show this would be possible by revising the input query through a bias-aware pseudo-relevance feedback framework. We report our findings based on four widely used TREC corpora namely Robust04, Gov2, ClueWeb09 and ClueWeb12 and using two classes of bias metrics. The findings of this paper are significant as they are among the first to show that decrease in bias does not necessarily need to come at the cost of reduced utility.

Supplementary Material

MP4 File (SIGIR2021_Video_Presentation.mp4)

Various researchers have recently explored the impact of different types of biases on information retrieval tasks such as ad hoc retrieval and question answering. While the impact of bias needs to be controlled in order to avoid increased prejudices, the literature has often viewed the relationship between increased retrieval utility (effectiveness) and reduced bias as a tradeoff where one can suffer from the other. In this paper, we empirically study this tradeoff and explore whether it would be possible to reduce bias while maintaining similar retrieval utility. We show this would be possible by revising the input query through a bias-aware pseudo-relevance feedback framework. We report our findings based on four widely used TREC corpora namely Robust04, Gov2, ClueWeb09 and ClueWeb12 and using two classes of bias metrics. The findings of this paper are significant as they are among the first to show that decrease in bias does not necessarily need to come at the cost of reduced utility.

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Cited By

Klasnja AArabzadeh NMehrvarz MBagheri EBaeza-Yates RWeller KPortela MSeneviratne OWeber IYasseri TBon ASrinivas SIbáñez L(2022)On the Characteristics of Ranking-based Gender Bias MeasuresProceedings of the 14th ACM Web Science Conference 202210.1145/3501247.3531540(245-249)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1145/3501247.3531540
Bigdeli AArabzadeh NSeyedSalehi SZihayat MBagheri EAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Gender Fairness in Information Retrieval SystemsProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532680(3436-3439)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532680
Zerveas GRekabsaz NCohen DEickhoff CAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Mitigating Bias in Search Results Through Contextual Document Reranking and Neutrality RegularizationProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531891(2532-2538)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531891
Show More Cited By

Index Terms

On the Orthogonality of Bias and Utility in Ad hoc Retrieval
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Presentation of retrieval results

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Published In

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

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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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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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Klasnja AArabzadeh NMehrvarz MBagheri EBaeza-Yates RWeller KPortela MSeneviratne OWeber IYasseri TBon ASrinivas SIbáñez L(2022)On the Characteristics of Ranking-based Gender Bias MeasuresProceedings of the 14th ACM Web Science Conference 202210.1145/3501247.3531540(245-249)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1145/3501247.3531540
Bigdeli AArabzadeh NSeyedSalehi SZihayat MBagheri EAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Gender Fairness in Information Retrieval SystemsProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532680(3436-3439)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532680
Zerveas GRekabsaz NCohen DEickhoff CAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Mitigating Bias in Search Results Through Contextual Document Reranking and Neutrality RegularizationProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531891(2532-2538)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531891
Seyedsalehi SBigdeli AArabzadeh NZihayat MBagheri EAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Addressing Gender-related Performance Disparities in Neural RankersProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531882(2484-2488)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531882
Bigdeli AArabzadeh NSeyedsalehi SZihayat MBagheri E(2022)A Light-Weight Strategy for Restraining Gender Biases in Neural RankersAdvances in Information Retrieval10.1007/978-3-030-99739-7_6(47-55)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-030-99739-7_6

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