T ag D ebias: Entity and Concept Tagging for Social Bias Mitigation in Pretrained Language Models (2024)

Abstract: Pre-trained language models (PLMs) play a crucial role in various applications, including sensitive domains such as the hiring process. However, extensive research has unveiled that these models tend to replicate social biases present in their pre-training data, raising ethical concerns. In this study, we propose the TagDebias method, which proposes debiasing a dataset using type tags. It then proceeds to fine-tune PLMs on this debiased dataset. Experiments show that our proposed TagDebias model, when applied to a ranking task, exhibits significant improvements in bias scores.

## introduction pre-trained language models (plms) are extensively utilized in various natural language processing tasks, acquiring a significant amount of knowledge during their pre-training phase. research has highlighted that these models often inherit substantial social biases present in their pre-training corpora, which may subsequently emerge in the outcomes of downstream tasks @xcite . so, it is crucial to identify and mitigate social bias in these models. there are different ways to mitigate social bias both in datasets and pre-trained models. state-ofthe-art approaches show effective debiasing methods in plms such as increasing dropout regularization @xcite , projection-based debiasing @xcite , and self-debias @xcite as a post-hoc method to discourage models from generating toxic sentences. other data-based bias mitigation methods such as counterfactual data augmentation (cda) @xcite or biased terms removal (scrubbing) @xcite have been proposed but exhibit some limitations. producing counterfactual data and fine-tuning pre-trained language models (plms) on an augmented dataset is resource-consuming and, in some cases, impossible. for example, generating a counterfactual example for the sentence "women gave birth" is impossible. scrubbing biased words removes contextual associations within the plms and can decrease model performance in downstream tasks. in this paper, we propose a framework for mitigating bias in datasets and pre-trained language models by tagging the biasinbios dataset @xcite , which is designed to examine gender-profession social biases. more specifically, we propose an approach named "tagdebias" to debias datasets by tagging gender indicator terms. the idea is to replace gender terms with semantic types that represent neutral terms for binary genders (female and male). we then fine-tune pretrained language models on the debiased dataset, teaching them that each gender term corresponds to the same neutral tag. the proposed method, "tagdebias," has the advantage of not requiring counterfactual data while maintaining model performance compared to the scrubbing method. furthermore, it outperforms data-based bias mitigation methods, specifically scrubbing and counterfactual data augmentation. to assess the fairness of the debiased models, we test our tagdebias model on a ranking task in the domain of biographies' ranking given a target job title. in this study, we will answer the following research questions: q1 does tagging stereotypical gender terms mitigate social bias in plms? q2 does tagging stereotypical gender terms worsen plms' performance? q3 does our proposed tagdebias model have a fairer ranking compared to base and scrubbed plms? in response to q1, we assess models with various tagging subsets using fairness classification metrics. our findings reveal that the "gender-specificterm" model surpasses both the initial and scrubbed models. evaluating model performance on the bi-asinbios dataset (q2), we observe that the tagging approach does not adversely affect model performance. finally, the tagdebias model demonstrates a substantial enhancement in fairness rankings, exhibiting an improvement compared to the initial and scrubbed models, respectively (q3). ## conclusion