De-Biasing Relevance Judgements for Fair Ranking - Bigdeli, Amin; Arabzadeh, Negar; Seyedsalehi, Shirin; Mitra, Bhaskar; Zihayat, Morteza; Bagheri, Ebrahim (Proceedings of the 45th European Conference on Information Retrieval (ECIR), 2023)
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.