@inproceedings{bell-etal-2025-role,
    title = "On the Role of Speech Data in Reducing Toxicity Detection Bias",
    author = "Bell, Samuel  and
      Meglioli, Mariano Coria  and
      Richards, Megan  and
      S{\'a}nchez, Eduardo  and
      Ropers, Christophe  and
      Wang, Skyler  and
      Williams, Adina  and
      Sagun, Levent  and
      Costa-juss{\`a}, Marta R.",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.naacl-long.67/",
    pages = "1454--1468",
    ISBN = "979-8-89176-189-6",
    abstract = "Text toxicity detection systems exhibit significant biases, producing disproportionate rates of false positives on samples mentioning demographic groups. But what about toxicity detection in speech? To investigate the extent to which text-based biases are mitigated by speech-based systems, we produce a set of high-quality group annotations for the multilingual MuTOX dataset, and then leverage these annotations to systematically compare speech- and text-based toxicity classifiers. Our findings indicate that access to speech data during inference supports reduced bias against group mentions, particularly for ambiguous and disagreement-inducing samples. Our results also suggest that improving classifiers, rather than transcription pipelines, is more helpful for reducing group bias. We publicly release our annotations and provide recommendations for future toxicity dataset construction."
}