short-paper

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

Ratz LSchedl MKopeinik SRekabsaz N(2024)Measuring Bias in Search Results Through Retrieval List ComparisonAdvances in Information Retrieval10.1007/978-3-031-56069-9_2(20-34)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56069-9_2
Abolghasemi AAzzopardi LAskari Ade Rijke MVerberne S(2024)Measuring Bias in a Ranked List Using Term-Based RepresentationsAdvances in Information Retrieval10.1007/978-3-031-56069-9_1(3-19)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56069-9_1
Krieg KParada-Cabaleiro EMedicus GLesota OSchedl MRekabsaz N(2023)Grep-BiasIR: A Dataset for Investigating Gender Representation Bias in Information Retrieval ResultsProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578295(444-448)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578295
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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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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

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

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Cited By

Ratz LSchedl MKopeinik SRekabsaz N(2024)Measuring Bias in Search Results Through Retrieval List ComparisonAdvances in Information Retrieval10.1007/978-3-031-56069-9_2(20-34)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56069-9_2
Abolghasemi AAzzopardi LAskari Ade Rijke MVerberne S(2024)Measuring Bias in a Ranked List Using Term-Based RepresentationsAdvances in Information Retrieval10.1007/978-3-031-56069-9_1(3-19)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56069-9_1
Krieg KParada-Cabaleiro EMedicus GLesota OSchedl MRekabsaz N(2023)Grep-BiasIR: A Dataset for Investigating Gender Representation Bias in Information Retrieval ResultsProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578295(444-448)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578295
Kopeinik SMara MRatz LKrieg KSchedl MRekabsaz N(2023)Show me a "Male Nurse"! How Gender Bias is Reflected in the Query Formulation of Search Engine UsersProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580863(1-15)Online publication date: 19-Apr-2023
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Bigdeli AArabzadeh NSeyedsalehi SZihayat MBagheri E(2023)Understanding and Mitigating Gender Bias in Information Retrieval SystemsAdvances in Information Retrieval10.1007/978-3-031-28241-6_32(315-323)Online publication date: 2-Apr-2023
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Jaenich TMcDonald GOunis I(2023)ColBERT-FairPRF: Towards Fair Pseudo-Relevance Feedback in Dense RetrievalAdvances in Information Retrieval10.1007/978-3-031-28238-6_36(457-465)Online publication date: 2-Apr-2023
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Bigdeli AArabzadeh NSeyedsalehi SMitra BZihayat MBagheri E(2023)De-biasing Relevance Judgements for Fair RankingAdvances in Information Retrieval10.1007/978-3-031-28238-6_24(350-358)Online publication date: 2-Apr-2023
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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
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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
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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
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