@inproceedings{ivetta-etal-2025-heseia,
    title = "{HESEIA}: A community-based dataset for evaluating social biases in large language models, co-designed in real school settings in {L}atin {A}merica",
    author = "Ivetta, Guido  and
      Gomez, Marcos J  and
      Martinelli, Sof{\'i}a  and
      Palombini, Pietro  and
      Echeveste, M Emilia  and
      Mazzeo, Nair Carolina  and
      Busaniche, Beatriz  and
      Benotti, Luciana",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1275/",
    pages = "25106--25128",
    ISBN = "979-8-89176-332-6",
    abstract = "Most resources for evaluating social biases in Large Language Models are developed without co-design from the communities affected by these biases, and rarely involve participatory approaches. We introduce HESEIA, a dataset of 46,499 sentences created in a professional development course. The course involved 370 high-school teachers and 5,370 students from 189 Latin-American schools. Unlike existing benchmarks, HESEIA captures intersectional biases across multiple demographic axes and school subjects. It reflects local contexts through the lived experience and pedagogical expertise of educators. Teachers used minimal pairs to create sentences that express stereotypes relevant to their school subjects and communities. We show the dataset diversity in term of demographic axes represented and also in terms of the knowledge areas included. We demonstrate that the dataset contains more stereotypes unrecognized by current LLMs than previous datasets. HESEIA is available to support bias assessments grounded in educational communities."
}