@inproceedings{kumar-etal-2025-mitigating,
    title = "Mitigating Gender Bias in Job Ranking Systems Using Job Advertisement Neutrality",
    author = "Kumar, Deepak  and
      Masoudian, Shahed  and
      Melchiorre, Alessandro B.  and
      Schedl, Markus",
    editor = "Atwell, Katherine  and
      Biester, Laura  and
      Borah, Angana  and
      Dementieva, Daryna  and
      Ignat, Oana  and
      Kotonya, Neema  and
      Liu, Ziyi  and
      Wan, Ruyuan  and
      Wilson, Steven  and
      Zhao, Jieyu",
    booktitle = "Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.nlp4pi-1.23/",
    pages = "264--271",
    ISBN = "978-1-959429-19-7",
    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."
}