Abstract
The objective of this paper is to show that it is possible to significantly reduce stereotypical gender biases in neural rankers without modifying the ranking loss function, which is the current approach in the literature. We systematically de-bias gold standard relevance judgement datasets with a set of balanced and well-matched query pairs. Such a de-biasing process will expose neural rankers to comparable queries from across gender identities that have associated relevant documents with compatible degrees of gender bias. Therefore, neural rankers will learn not to associate varying degrees of bias to queries from certain gender identities. Our experiments show that our approach is able to (1) systematically reduces gender biases associated with different gender identities, and (2) at the same time maintain the same level of retrieval effectiveness.
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References
Baeza-Yates, R.: Bias on the web. Commun. ACM 61(6), 54–61 (2018)
Baeza-Yates, R.: Bias in search and recommender systems. In: Fourteenth ACM Conference on Recommender Systems, p. 2 (2020)
Bigdeli, A., Arabzadeh, N., Seyedsalehi, S., Zihayat, M., Bagheri, E.: A light-weight strategy for restraining gender biases in neural rankers. In: Hagen, M., et al. (eds.) ECIR 2022. LNCS, vol. 13186, pp. 47–55. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99739-7_6
Bigdeli, A., Arabzadeh, N., Seyersalehi, S., Zihayat, M., Bagheri, E.: 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 (2021)
Bigdeli, A., Arabzadeh, N., Zihayat, M., Bagheri, E.: Exploring gender biases in information retrieval relevance judgement datasets. In: Hiemstra, D., Moens, M.-F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) ECIR 2021. LNCS, vol. 12657, pp. 216–224. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72240-1_18
Caliskan, A., Bryson, J.J., Narayanan, A.: Semantics derived automatically from language corpora contain human-like biases. Science 356(6334), 183–186 (2017)
Fabris, A., Purpura, A., Silvello, G., Susto, G.A.: Gender stereotype reinforcement: measuring the gender bias conveyed by ranking algorithms. Inf. Process. Manag. 57(6), 102377 (2020)
Font, J.E., Costa-Jussa, M.R.: Equalizing gender biases in neural machine translation with word embeddings techniques. arXiv preprint arXiv:1901.03116 (2019)
Gerritse, E.J., Hasibi, F., de Vries, A.P.: Bias in conversational search: the double-edged sword of the personalized knowledge graph. In: Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval, pp. 133–136 (2020)
Ju, J.H., Yang, J.H., Wang, C.J.: Text-to-text multi-view learning for passage re-ranking. arXiv preprint arXiv:2104.14133 (2021)
Klasnja, A., Arabzadeh, N., Mehrvarz, M., Bagheri, E.: On the characteristics of ranking-based gender bias measures. In: 14th ACM Web Science Conference 2022, pp. 245–249 (2022)
Li, J., Ott, M., Cardie, C., Hovy, E.: Towards a general rule for identifying deceptive opinion spam. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1566–1576 (2014)
Liu, Z., Zhang, K., Xiong, C., Liu, Z.: OpenMatch: an open-source package for information retrieval. arXiv e-prints pp. arXiv-2102 (2021)
Nguyen, T., et al.: MS MARCO: a human generated machine reading comprehension dataset. In: CoCo@ NIPS (2016)
Nogueira, R., Cho, K.: Passage re-ranking with BERT. arXiv preprint arXiv:1901.04085 (2019)
Nogueira, R., Lin, J., Epistemic, A.: From doc2query to docTTTTTquery. Online preprint (2019)
Olteanu, A., et al.: FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. In: ACM SIGIR Forum, vol. 53, pp. 20–43. ACM, New York (2021)
Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. arXiv preprint arXiv:1107.4557 (2011)
Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count: LIWC 2001. 71(2001) (2001)
Rekabsaz, N., Kopeinik, S., Schedl, M.: Societal biases in retrieved contents: measurement framework and adversarial mitigation for BERT rankers. arXiv preprint arXiv:2104.13640 (2021)
Rekabsaz, N., Schedl, M.: Do neural ranking models intensify gender bias? In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2065–2068 (2020)
Seyedsalehi, S., Bigdeli, A., Arabzadeh, N., Mitra, B., Zihayat, M., Bagheri, E.: Bias-aware fair neural ranking for addressing stereotypical gender biases. In: EDBT, pp. 2–435 (2022)
Seyedsalehi, S., Bigdeli, A., Arabzadeh, N., Zihayat, M., Bagheri, E.: Addressing gender-related performance disparities in neural rankers. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2484–2488 (2022)
Sun, T., et al.: Mitigating gender bias in natural language processing: literature review. arXiv preprint arXiv:1906.08976 (2019)
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Bigdeli, A., Arabzadeh, N., Seyedsalehi, S., Mitra, B., Zihayat, M., Bagheri, E. (2023). De-biasing Relevance Judgements for Fair Ranking. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13981. Springer, Cham. https://doi.org/10.1007/978-3-031-28238-6_24
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