分析大模型生成的照护机器人回应，发现对残障、年龄等群体存在偏见。
Encoding Inequity: Examining Demographic Bias in LLM-Driven Robot Caregiving
- 用不同人口特征提示大模型，观察机器人回应差异
- 残障和老年群体描述更简单，LGBTQ+群体情感更低
- 回应模式强化刻板印象，警示人机交互设计需包容
随着机器人承担照护角色，确保与多样化人群的公平无偏互动至关重要。尽管大型语言模型（LLMs）在塑造机器人行为、言语和决策中起关键作用，但这些模型可能编码并传播社会偏见，导致基于性别、性取向、种族、族裔、国籍、残疾和年龄等因素的照护差异。本文研究了在不同人口特征提示下，LLM生成的回应如何影响机器人照护特性和责任。结果显示：对残障和年龄相关描述趋于简化，对残障及LGBTQ+身份的情感评分较低，且回应呈现强化刻板印象的聚类模式。研究强调了伦理化、包容性人机交互设计的必要性。
原文摘要 · Abstract (English)
As robots take on caregiving roles, ensuring equitable and unbiased interactions with diverse populations is critical. Although Large Language Models (LLMs) serve as key components in shaping robotic behavior, speech, and decision-making, these models may encode and propagate societal biases, leading to disparities in care based on demographic factors. This paper examines how LLM-generated responses shape robot caregiving characteristics and responsibilities when prompted with different demographic information related to sex, gender, sexuality, race, ethnicity, nationality, disability, and age. Findings show simplified descriptions for disability and age, lower sentiment for disability and LGBTQ+ identities, and distinct clustering patterns reinforcing stereotypes in caregiving narratives. These results emphasize the need for ethical and inclusive HRI design.
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