@inproceedings{ungless-etal-2022-robust,
    title = "A Robust Bias Mitigation Procedure Based on the Stereotype Content Model",
    author = {Ungless, Eddie  and
      Rafferty, Amy  and
      Nag, Hrichika  and
      Ross, Bj{\"o}rn},
    editor = "Bamman, David  and
      Hovy, Dirk  and
      Jurgens, David  and
      Keith, Katherine  and
      O'Connor, Brendan  and
      Volkova, Svitlana",
    booktitle = "Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)",
    month = nov,
    year = "2022",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.nlpcss-1.23/",
    doi = "10.18653/v1/2022.nlpcss-1.23",
    pages = "207--217",
    abstract = "The Stereotype Content model (SCM) states that we tend to perceive minority groups as cold, incompetent or both. In this paper we adapt existing work to demonstrate that the Stereotype Content model holds for contextualised word embeddings, then use these results to evaluate a fine-tuning process designed to drive a language model away from stereotyped portrayals of minority groups. We find the SCM terms are better able to capture bias than demographic agnostic terms related to pleasantness. Further, we were able to reduce the presence of stereotypes in the model through a simple fine-tuning procedure that required minimal human and computer resources, without harming downstream performance. We present this work as a prototype of a debiasing procedure that aims to remove the need for a priori knowledge of the specifics of bias in the model."
}