Mitigating Gender Bias in Job Ranking Systems Using Job Advertisement Neutrality (2025)

Abstract: Transformer-based Job Ranking Systems (JRSs) are vulnerable to societal biases inherited in unbalanced datasets. These biases often manifest as unjust job rankings, particularly disadvantaging candidates of different genders. Most bias mitigation techniques leverage candidates’ gender and align gender distributions within the embeddings of JRSs to mitigate bias. While such methods effectively align distributional properties and make JRSs agnostic to gender, they frequently fall short in addressing empirical fairness metrics, such as the performance gap across genders. In this study, we shift our attention from candidate gender to mitigate bias based on gendered language in job advertisements. We propose a novel neutrality score based on automatically discovered biased words in job ads and use it to re-rank the model’s decisions. We evaluate our method by comparing it with different bias mitigation strategies and empirically demonstrate that our proposed method not only improves fairness but can also enhance the model’s performance.

## introduction nowadays, transformer-based language models (lms) are being used for a variety of tasks such as document classification @xcite , information retrieval @xcite ), text generation @xcite , and recommender systems (recsys) @xcite . despite their effectiveness, these models tend to inherit societal biases (e.g., gender bias) present in their training data. recent studies have concentrated on analyzing the impact of these biases on model decision-making and developing strategies to mitigate them, through preprocessing @xcite , in-processing @xcite , or post-processing @xcite . among various applications, the usage of * these authors contributed equally to this work lms as recsys, particularly as job ranking systems (jrss), is of significant importance. minor alterations in the ranking of jrss with the contribution of the sensitive attributes can lead to discrimination against certain demographic groups (e.g., females or older individuals). research in this domain has focused on leveraging encoder or decoder lms to reduce bias in the job advertisement recommendations for various demographic groups @xcite . common mitigation strategies often utilize candidates' sensitive attributes (e.g., gender, age), as labeled data to render the model's embeddings agnostic to target attributes @xcite . in this study, we propose a novel approach that leverages implicit bias within job advertisements to mitigate gender bias on encoder lms. instead of relying on candidates' gender, we introduce a new neutrality score, calculated based on implicit biased terms that are automatically derived from job advertisements. we implement our method on two encoder lms namely bert-base and distillroberta following previous works and due to their strong contextual understanding and representational power to encode natural language. we evaluate our proposed re-ranking strategy and compare it with other successful bias mitigation techniques. our findings demonstrate that our proposed method not only enhances the model's fairness but can also yield improvements in performance on the primary task-an outcome not achieved by other methods. in summary, our contributions are as follows: (1) we introduce a novel neutrality score derived from implicit biased terms present in job advertisements. (2) we demonstrate that re-ranking jobs according to our neutrality score enhances both fairness and task performance. the code for our study is available at the following link: github. ## related work jrss, similar to lms, suffer from various societal biases @xcite and have been investigated in the past on various popular platforms @xcite @xcite . the mitigation approaches for these biases are mostly focused on pre-processing approaches @xcite , such as replacing gendered pronouns with gender-neutral pronouns @xcite or directing candidates to dedicated jrss for particular attributes @xcite . @xcite also try in-processing bias mitigation using adversarial debiasing. they tried to make hidden representation agnostic to the candidate's gender adversarially. a post-processing debiasing of jrs is investigated by @xcite through reranking the model output based on the candidate's gender to achieve a fairness constraint over the whole dataset. the work most closely related to ours is that of @xcite , who introduced neutrality score based on explicit bias words derived from a pre-defined dictionary to enforce neutrality in information retrieval. our approach diverges from theirs in several key aspects. firstly, our focus is implicit gendered language in job advertisements, building on the methodology established by @xcite for candidate ranking systems. additionally, we formulate our neutrality score based on the biasedness of words rather than relying solely on binary gendered terms. lastly, we apply our neutrality score directly to the ranking process of the model, enhancing both its performance and neutrality. ## methodology to find better representation between genders, we introduce a three-stage approach: (1) we acquire the biased words in job advertisements and their biasedness(section 3.1). (2) we use specific words assigned for each class of job and introduce a neutrality score based on their biasedness and frequency of usage in job advertisements(section 3.2). (3) we utilize the new job advertisement neutrality score to re-rank the jobs. ## results as it can be seen from ## conclusions in this study, we address bias in job ranking systems by introducing a novel neutrality score using the biasedness of words present in job advertisements. we employed this neutrality score as a reranking strategy following evaluation and demonstrated its effectiveness in enhancing model performance. our results show that integrating the neutrality score not only mitigates bias but also improves overall performance metrics, offering an easy and effective approach to job ranking. in the future, we plan to target non-binary gender.