@inproceedings{anastasiou-etal-2024-luxembourgish,
    title = "A {L}uxembourgish Corpus as a Gender Bias Evaluation Testset",
    author = "Anastasiou, Dimitra  and
      Blond-Hanten, Carole  and
      Gallais, Marie",
    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://preview.aclanthology.org/jlcl-multiple-ingestion/2024.lrec-main.70/",
    pages = "784--788",
    abstract = "According to the United Nations Development Programme, gender inequality is a metric that is composed of three dimensions: reproductive health, empowerment, and the labour market. Gender inequality is an obstacle to equal opportunities in society as a whole. In this paper we present our work-in-progress of designing and playing a physical game with digital elements. We currently conduct Conversation Analysis of transcribed speech of 58567 words and documenting bias. We also test OpenAI`s ChatGPT for bias in quiz-like gender-related questions."
}