Hundreds of millions of people rely on large language models (LLMs) for education, work, and even healthcare. Yet these models are known to reproduce and amplify social biases present in their training data. Moreover, text-based interfaces remain a barrier for many, for example, users with limited literacy, motor impairments, or mobile-only devices. Voice interaction promises to expand accessibility, but unlike text, speech carries identity cues that users cannot easily mask, raising concerns about whether accessibility gains may come at the cost of equitable treatment. Here we show that audio-enabled LLMs exhibit systematic gender discrimination, shifting responses toward gender-stereotyped adjectives and occupations solely on the basis of speaker voice, and amplifying bias beyond that observed in text-based interaction. Thus, voice interfaces do not merely extend text models to a new modality but introduce distinct bias mechanisms tied to paralinguistic cues. Complementary survey evidence ($n=1,000$) shows that infrequent chatbot users are most hesitant to undisclosed attribute inference and most likely to disengage when such practices are revealed. To demonstrate a potential mitigation strategy, we show that pitch manipulation can systematically regulate gender-discriminatory outputs. Overall, our findings reveal a critical tension in AI development: efforts to expand accessibility through voice interfaces simultaneously create new pathways for discrimination, demanding that fairness and accessibility be addressed in tandem.

翻译：数以亿计的用户依赖大型语言模型（LLMs）进行教育、工作乃至医疗保健。然而，这些模型已知会复制并放大其训练数据中存在的社会偏见。此外，文本界面仍是许多人的障碍，例如识字能力有限、存在运动障碍或仅使用移动设备的用户。语音交互有望提升可及性，但与文本不同，语音携带用户难以掩饰的身份线索，这引发担忧：可及性的提升是否可能以公平待遇为代价。我们在此表明，支持音频的LLMs表现出系统性性别歧视——仅基于说话者声音便将回应偏向性别刻板印象的形容词和职业，且其放大偏见的程度超过文本交互中的观察结果。因此，语音界面不仅是将文本模型扩展至新模态，更是引入了与副语言线索相关的独特偏见机制。配套的调查证据（$n=1,000$）显示，不常使用聊天机器人的用户对未公开属性推断最为抗拒，且当此类行为被揭露时最可能停止使用。为展示潜在缓解策略，我们证实音高操控可系统性地调节性别歧视性输出。总体而言，我们的发现揭示了AI发展中的关键矛盾：通过语音界面扩展可及性的努力同时创造了新的歧视途径，这要求公平性与可及性必须协同解决。