Shirin Seyedsalehi
Negar Arabzadeh
ABSTRACT
While neural rankers continue to show notable performance improvements over a wide variety of information retrieval tasks, there have been recent studies that show such rankers may intensify certain stereotypical biases. In this paper, we investigate whether neural rankers introduce retrieval effectiveness (performance) disparities over queries related to different genders. We specifically study whether there are significant performance differences between male and female queries when retrieved by neural rankers. Through our empirical study over the MS MARCO collection, we find that such performance disparities are notable and that the performance disparities may be due to the difference between how queries and their relevant judgements are collected and distributed for different gendered queries. More specifically, we observe that male queries are more closely associated with their relevant documents compared to female queries and hence neural rankers are able to more easily learn associations between male queries and their relevant documents. We show that it is possible to systematically balance relevance judgment collections in order to reduce performance disparity between different gendered queries without negatively compromising overall model performance.
Supplemental Material
- Amin Bigdeli, Negar Arabzadeh, Shirin Seyedsalehi, Morteza Zihayat, and Ebrahim Bagheri. 2022. A Light-Weight Strategy for Restraining Gender Biases in Neural Rankers. In European Conference on Information Retrieval. Springer, 47--55.Google Scholar
Digital Library
- Amin Bigdeli, Negar Arabzadeh, Shirin Seyersalehi, Morteza Zihayat, and Ebrahim Bagheri. 2021. On the Orthogonality of Bias and Utility in Ad hoc Retrieval. In Proceedings of the 44rd International ACM SIGIR Conference on Research and Development in Information Retrieval.Google ScholarDigital Library
- Amin Bigdeli, Negar Arabzadeh, Morteza Zihayat, and Ebrahim Bagheri. 2021. Exploring Gender Biases in Information Retrieval Relevance Judgement Datasets. In Advances in Information Retrieval - 43rd European Conference on IR Research.Google Scholar
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- Shirin Seyedsalehi, Amin Bigdeli, Negar Arabzadeh, Bhaskar Mitra, Morteza Zihayat, and Ebrahim Bagheri. 2022. Bias-aware Fair Neural Ranking for Addressing Stereotypical Gender Biases. In EDBT/ICDT 2022 (2022-01-01).Google Scholar
Index Terms
- Addressing Gender-related Performance Disparities in Neural Rankers
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