@inproceedings{dewulf-2025-evaluating,
    title = "Evaluating Gender Bias in {D}utch {NLP}: Insights from {R}ob{BERT}-2023 and the {HONEST} Framework",
    author = "Dewulf, Marie",
    editor = "Hackenbuchner, Jani{\c{c}}a  and
      Bentivogli, Luisa  and
      Daems, Joke  and
      Manna, Chiara  and
      Savoldi, Beatrice  and
      Vanmassenhove, Eva",
    booktitle = "Proceedings of the 3rd Workshop on Gender-Inclusive Translation Technologies (GITT 2025)",
    month = jun,
    year = "2025",
    address = "Geneva, Switzerland",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.gitt-1.7/",
    pages = "91--92",
    ISBN = "978-2-9701897-4-9",
    abstract = "This study investigates gender bias in the Dutch RobBERT-2023 language model using an adapted version of the HONEST framework, which assesses harmful sentence completions. By translating and expanding HONEST templates to include non-binary and gender-neutral language, we systematically evaluate whether RobBERT-2023 exhibits biased or harmful outputs across gender identities. Our findings reveal that while the model{'}s overall bias score is relatively low, non-binary identities are disproportionately affected by derogatory language."
}