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.

翻译：性别误称是指使用与个人自我认同性别不符的称谓指代他人的行为。这种行为会边缘化并削弱个体的自我认同感，造成严重伤害。基于英语的方法（例如使用代词"they"）已形成明确的避免性别误称方案。然而，由于语法结构和文化背景的差异，其他语言面临着独特的挑战。本研究采用参与式设计方法，开发了涵盖42种语言及方言的性别误称评估与缓解方法，旨在为所有语言设计有效且恰当的防护机制。我们在标准的大型语言模型应用（会议转录摘要生成）中测试了这些防护机制，其数据生成与标注步骤均采用人机协同方法。研究发现，所提出的防护机制能显著降低所有语言生成摘要中的性别误称率，且不损害摘要质量。我们的人机协同方法展示了一种可行方案，能够将包容且负责任的人工智能解决方案扩展到多语言与多文化场景。