对比人类与大模型生成的主题分析，评估其在弱势群体人机交互研究中的有效性与伦理风险。
Evaluating Human and LLM-Generated Thematic Analysis in HRI for Vulnerable Populations: A Comparative and Ethical Analysis
- 比较人类与大模型生成主题的一致性与语义差异
- 发现模型生成主题存在系统性偏差，可能误读脆弱群体经历
- 提醒研究者警惕大模型辅助分析的伦理隐患，尤其在敏感场景
主题分析（TA）长期被视为一种内在的人类、反思性与解释性过程。然而，大模型生成的TA在涉及弱势群体的人机交互（HRI）研究中的适用性仍缺乏充分检验，尤其是在敏感研究背景下，其有效性和伦理问题值得深思。本文通过对比人类与大模型生成的主题分析，评估二者在客观一致性和语义层面的匹配度，并探究观察到的差异是否反映具有伦理意义的系统性解释模式。研究重点考察大模型生成的主题分析是否会边缘化或歪曲弱势参与者的真实体验，为采用大模型辅助主题分析的HRI研究提供重要警示。
原文摘要 · Abstract (English)
Thematic analysis (TA) has long been regarded as an inherently human, reflexive, and interpretive process. However, the extent to which LLM-generated TA is appropriate for Human-Robot Interaction (HRI) research involving vulnerable populations remains largely unexamined and raises critical questions about validity and ethics, particularly in sensitive research contexts. This paper presents a comparative study of human- and LLM-generated TA in an HRI context with a focus on vulnerable populations. We evaluate both objective and semantic agreement between human- and LLMgenerated themes, and examine whether observed divergences reflect systematic interpretive patterns with ethical significance. Our analysis investigates whether LLM-generated TA risks marginalising or misrepresenting the experiences of vulnerable participants, with implications for researchers employing LLM-assisted TA in HRI.
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