Debiasing Neural Retrieval via In-batch Balancing Regularization (2022)

Abstract: People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics. The in-processing fair ranking methods provides a trade-offs between accuracy and fairness through adding a fairness-related regularization term in the loss function. However, there haven’t been intuitive objective functions that depend on the click probability and user engagement to directly optimize towards this. In this work, we propose the I n- B atch B alancing R egularization (IBBR) to mitigate the ranking disparity among subgroups. In particular, we develop a differentiable normed Pairwise Ranking Fairness (nPRF) and leverage the T-statistics on top of nPRF over subgroups as a regularization to improve fairness. Empirical results with the BERT-based neural rankers on the MS MARCO Passage Retrieval dataset with the human-annotated non-gendered queries benchmark (CITATION) show that our IBBR method with nPRF achieves significantly less bias with minimal degradation in ranking performance compared with the baseline.

## introduction recent advancements in natural language processing and information retrieval @xcite @xcite have led to great progress in search performances. however, search engines easily expose various biases (e.g., @xcite @xcite ), which sabotage the trust of human beings from day to day. many methods have been proposed recently to reduce the bias of the retrievers. existing fairness-aware ranking methods can be categorized into pre-processing methods, in-processing methods, and post-processing methods @xcite . pre-processing methods typically focus on mitigating bias in data before training the model. @xcite discussed the individual fairness pre-processing method to learn the fair representation of data. however, the representation-based method will undermine the value of the features determined by domain experts @xcite . the in-processing methods usually transform the fairness in ranking task into an optimization problem consisting of an accuracy objective and a fairness objective. these methods learn the best balance between these two objectives @xcite @xcite . @xcite handles different types of bias without knowing the exact bias form; post-processing algorithms @xcite @xcite are model agnostic without requiring access to the training process, but these methods re-order the ranking at the expense of accuracy @xcite . among recent works on fair neural retrieval, @xcite introduce the pairwise ranking fairness (prf) metric for ranking predictions. this pairwise fairness metric evaluates whether there is a difference in accuracy between two groups. @xcite (advbert) mitigates the bias magnitude from the concatenation of query and passage text rather than treating the bias magnitude from query and passage separately through an adversarial neural network. in this paper, we propose the in-batch balancing regularization (ibbr) method combined with the neural retrieval model. ibbr is an in-processing debiasing method that balances the ranking disparity among different demographic groups by adding an in-batch balancing regularization term to the objective function. we design two batch-level regularization terms, pairwise difference (pd) and t-statistics (ts) that measure biases within demographic groups. in addition, we introduce normed pairwise ranking fairness (nprf), a relaxed version of the prf @xcite that is differentiable, thus could be directly optimized. we apply ibbr to ms marco passage re-ranking task @xcite on gender bias using pretrained bert l 2 and bert l 4 models @xcite . empirical results show that our model could achieve significantly less bias with minor ranking performance degradation, striking a good balance between accuracy and fairness. our contributions can be summarized as follows: ## problem definition we first introduce notations in the ranking task in §2.1. §2.2 provides the definition of the bias of the passage. in §2.3, we propose the definition of the group fairness in the ranking task. ## algorithms in this section, we create a regularizer based on the nprf to mitigate the gender bias. in §3.1, we introduce necessary components for the neural retrieval task. in §3.2, we provide the definition of the ranking loss and two fairness loss functions, pairwise difference loss and t-statistics loss, acting as a regularizer, named as in-batch balancing regularization method (ibbr). ## experiments in this section, we describe data resources in §4.1, experiment setup in §4.2, evaluation metrics in section 4.3, baseline models in section 4.4, and corresponding result analysis in section 4.5. ## conclusion in this paper, we present a novel in-processing inbatch balancing regularization method to mitigate ranking disparity and retain ranking performance. we also overcome the non-differentiable and nonconvex properties of the 0-normed prf and propose the nprf. we conduct experiments on the ms marco dataset and find that the nprf with tstatistics regularization method outperforms other methods in terms of fairness metrics and ranking metrics. in future work, we will consider generalizing our method to multiple protected variables such as age, income, etc, and also addressing bias in the query by employing adversarial networks.