On the Role of Speech Data in Reducing Toxicity Detection Bias
Samuel Bell, Mariano Coria Meglioli, Megan Richards, Eduardo Sánchez, Christophe Ropers, Skyler Wang, Adina Williams, Levent Sagun
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.
BibTeX
@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://aclanthology.org/2025.naacl-long.67/",
pages = "1454--1468",
ISBN = "979-8-89176-189-6"
}