Grep-BiasIR: A dataset for investigating gender representation bias in information retrieval results
Open accessAbstract
The provided contents by information retrieval (IR) systems can reflect the existing societal biases and stereotypes. Such biases in retrieval results can lead to further establishing and strengthening stereotypes in society and also in the systems. To facilitate the studies of gender bias in the retrieval results of IR systems, we introduce Gender Representation-Bias for Information Retrieval (Grep-BiasIR), a novel thoroughly-audited dataset consisting of 118 bias-sensitive neutral search queries. The set of queries covers a wide range of gender-related topics, for which a biased representation of genders in the search result can be considered as socially problematic. Each query is accompanied with one relevant and one non-relevant document, where the document is also provided in three variations of female, male, and neutral. The dataset is available at https://github.com/KlaraKrieg/GrepBiasIR.
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Grep-BiasIR provides 118 gender-neutral search queries for document retrieval to assess gender representation bias.
“Gender Representation-Bias for Information Retrieval (GrepBiasIR) (Krieg et al. 2023) provides 118 gender-neutral search queries for document retrieval to assess gender representation bias.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Krieg et al. 2023
Grep-BiasIR pairs each query with a relevant and nonrelevant document with feminine, masculine, and neutral variations, with 708 documents in total.
“Instead of providing associated answers as done with question-answering, Grep-BiasIR pairs each query with a relevant and nonrelevant document with feminine, masculine, and neutral variations, with 708 documents in total.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Krieg et al. 2023
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Cites
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“The search results of IR systems strongly affect the perception of users, and influence what is conceived as the “state of theworld” [13].”
BackgroundUnequal Representation and Gender Stereotypes in Image Search Results for Occupations
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Chen, L., Ma, R., Hannák, A., & Wilson, C. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174225 · Cited by 155 · see citing works
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Bias and Fairness in Large Language Models: A Survey
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00524 · Cited by 665 · see citing works
Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models
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Unlearning Protected User Attributes in Recommendations with Adversarial Training
Ganhör, C., Penz, D., Rekabsaz, N., Lesota, O., & Schedl, M. (2022) · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3477495.3531820 · Cited by 47 · see citing works
Self-citation
- Authors
- Krieg, K., Parada-Cabaleiro, E., Medicus, G., Lesota, O., Schedl, M., & Rekabsaz, N.
- Affiliations
- Universität Innsbruck (AT)
- Johannes Kepler University Linz, Austria
- Johannes Kepler University of Linz (AT)
- Venue
- Proceedings of the 2023 Conference on Human Information Interaction and Retrieval
- Publisher
- ACM
- Published
- 2023-03-19
- Pages
- 444-448
- OpenAlex
- W4327909986
- Cited by
- 31
- References
- 4
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 5 · 2023: 7 · 2022: 5
- FWCI
- 5.55
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Conference on Human Information Interaction and Retrieval (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Austrian Science Fund (P33526, LIT-2020-9-SEE-113, DFH 23)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Gender Studies › Media, Gender, and Advertising
- Keywords
- gender bias, information retrieval, search result bias, stereotypes, gender representation, datasets, social bias
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