@inproceedings{lucy-bamman-2021-gender,
    title = "Gender and Representation Bias in {GPT}-3 Generated Stories",
    author = "Lucy, Li  and
      Bamman, David",
    editor = "Akoury, Nader  and
      Brahman, Faeze  and
      Chaturvedi, Snigdha  and
      Clark, Elizabeth  and
      Iyyer, Mohit  and
      Martin, Lara J.",
    booktitle = "Proceedings of the Third Workshop on Narrative Understanding",
    month = jun,
    year = "2021",
    address = "Virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.nuse-1.5/",
    doi = "10.18653/v1/2021.nuse-1.5",
    pages = "48--55",
    abstract = "Using topic modeling and lexicon-based word similarity, we find that stories generated by GPT-3 exhibit many known gender stereotypes. Generated stories depict different topics and descriptions depending on GPT-3{'}s perceived gender of the character in a prompt, with feminine characters more likely to be associated with family and appearance, and described as less powerful than masculine characters, even when associated with high power verbs in a prompt. Our study raises questions on how one can avoid unintended social biases when using large language models for storytelling."
}