A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications

Misgendering is the act of referring to someone by a gender that does not match their chosen identity. It marginalizes and undermines a person's sense of self, causing significant harm. English-based approaches have clear-cut approaches to avoiding misgendering, such as the use of the pronoun ``they''. However, other languages pose unique challenges due to both grammatical and cultural constructs. In this work we develop methodologies to assess and mitigate misgendering across 42 languages and dialects using a participatory-design approach to design effective and appropriate guardrails across all languages. We test these guardrails in a standard large language model-based application (meeting transcript summarization), where both the data generation and the annotation steps followed a human-in-the-loop approach. We find that the proposed guardrails are very effective in reducing misgendering rates across all languages in the summaries generated, and without incurring loss of quality. Our human-in-the-loop approach demonstrates a method to feasibly scale inclusive and responsible AI-based solutions across multiple languages and cultures.

@article{sitaram2025_2503.20302,
  title={ A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications },
  author={ Sunayana Sitaram and Adrian de Wynter and Isobel McCrum and Qilong Gu and Si-Qing Chen },
  journal={arXiv preprint arXiv:2503.20302},
  year={ 2025 }
}