LGBTQIA+身份如何影响大语言模型的行为？对心理健康AI系统需求工程的启示
How Does LGBTQIA+ Identity Affect LLM Behavior? Implications for Requirements Engineering of Mental Health AI Systems
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中文总结 AI 辅助
该研究探究了LGBTQIA+身份披露对ChatGPT心理健康回应的影响，发现其未显著影响回应完整性与支持性，但会带来细微的情境解读差异，为心理健康AI系统的公平性需求提供启示。
中文摘要 AI 辅助
大语言模型现已成为医疗保健和心理健康支持系统的一部分，引发了对包括LGBTQIA+群体在内的弱势群体公平性的担忧。然而，很少有实证研究调查明确的LGBTQIA+身份披露如何影响心理健康场景下大语言模型生成的回应。本研究从Counsel Chat仓库中提取了50个真实的心理健康问题，为每个问题构建了三种提示条件：无身份披露、明确的顺性别异性恋身份披露、明确的LGBTQIA+身份披露。我们使用二元编码和比较分析，生成并分析了这些条件下的450个ChatGPT回应。研究发现，LGBTQIA+身份披露并未显著影响回应的完整性或支持性指导；但与其他两种条件相比，明确披露LGBTQIA+身份的条件下的回应表现出更多的身份认可、情境扩展、无依据的假设以及偶尔的刻板印象推理。这些结果表明，对话式AI系统中与公平性相关的担忧可能源于情境解读和解释性推理的细微差异，而非明显有害的输出。我们讨论了其对公平性需求以及基于大语言模型的心理健康支持系统开发的启示。
英文摘要
Large Language Models are now part of healthcare and mental health support systems, raising concerns regarding fairness toward vulnerable populations, including LGBTQIA+ individuals. However, limited empirical work has investigated how explicit LGBTQIA+ identity disclosure influences LLM-generated responses in mental health contexts. In this study, we extracted 50 real mental health questions from the Counsel Chat repository and constructed three prompt conditions for each question: no identity disclosure, explicit straight identity disclosure, and explicit LGBTQIA+ identity disclosure. We generated and analyzed 450 ChatGPT responses across these conditions using binary coding and comparative analysis. Our findings indicate that LGBTQIA+ identity disclosure did not substantially affect response completeness or supportive guidance. However, responses in the LGBTQIA+-explicit condition presented substantially more identity acknowledgment, contextual expansion, unsupported assumptions, and occasional stereotypical reasoning compared to both other conditions. These results suggest that fairness-related concerns in conversational AI systems may emerge through subtle differences in contextual interpretation and explanatory reasoning rather than through overtly harmful outputs. We discuss implications for fairness requirements and the development of LLM-based mental health support systems.
