@inproceedings{kim-etal-2024-able,
    title = "{ABLE}: Agency-{B}e{L}iefs Embedding to Address Stereotypical Bias through Awareness Instead of Obliviousness",
    author = "Kim, Michelle YoungJin  and
      Kim, Junghwan  and
      Johnson, Kristen",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.4",
    pages = "43--56",
    abstract = "Natural Language Processing (NLP) models tend to inherit and amplify stereotypical biases present in their training data, leading to harmful societal consequences. Current efforts to rectify these biases typically revolve around making models oblivious to bias, which is at odds with the idea that humans require increased awareness to tackle these biases better. This prompts a fundamental research question: are bias-oblivious models the only viable solution to combat stereotypical biases? This paper answers this question by proposing the Agency-BeLiefs Embedding (ABLE) model, a novel approach that actively encodes stereotypical biases into the embedding space. ABLE draws upon social psychological theory to acquire and represent stereotypical biases in the form of agency and belief scores rather than directly representing stereotyped groups. Our experimental results showcase ABLE{'}s effectiveness in learning agency and belief stereotypes while preserving the language model{'}s proficiency. Furthermore, we underscore the practical significance of incorporating stereotypes within the ABLE model by demonstrating its utility in various downstream tasks. Our approach exemplifies the potential benefits of addressing bias through awareness, as opposed to the prevailing approach of mitigating bias through obliviousness.",
}

Natural Language Processing (NLP) models tend to inherit and amplify stereotypical biases present in their training data, leading to harmful societal consequences. Current efforts to rectify these biases typically revolve around making models oblivious to bias, which is at odds with the idea that humans require increased awareness to tackle these biases better. This prompts a fundamental research question: are bias-oblivious models the only viable solution to combat stereotypical biases? This paper answers this question by proposing the Agency-BeLiefs Embedding (ABLE) model, a novel approach that actively encodes stereotypical biases into the embedding space. ABLE draws upon social psychological theory to acquire and represent stereotypical biases in the form of agency and belief scores rather than directly representing stereotyped groups. Our experimental results showcase ABLE{'}s effectiveness in learning agency and belief stereotypes while preserving the language model{'}s proficiency. Furthermore, we underscore the practical significance of incorporating stereotypes within the ABLE model by demonstrating its utility in various downstream tasks. Our approach exemplifies the potential benefits of addressing bias through awareness, as opposed to the prevailing approach of mitigating bias through obliviousness.
Kim, Michelle YoungJin Kim, Junghwan Johnson, Kristen
ABLE: Agency-BeLiefs Embedding to Address Stereotypical Bias through Awareness Instead of Obliviousness