Encoding Inequity: Examining Demographic Bias in LLM-Driven Robot Caregiving
This addresses bias in human-robot interaction for caregiving, which is an incremental but important domain-specific problem.
The paper investigated how demographic biases in Large Language Models (LLMs) affect robot caregiving, finding that LLM-generated responses showed simplified descriptions for disability and age, lower sentiment for disability and LGBTQ+ identities, and clustering patterns reinforcing stereotypes.
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.