Not Only WEIRD but “Uncanny”? A Systematic Review of Diversity in Human–Robot Interaction Research

恐怖谷理论 不可思议的 多样性(政治) 机器人 机器人学 认知科学 人机交互 人工智能 心理学 人机交互 计算机科学 沟通 艺术 美学 社会学 人类学
作者
Katie Seaborn,Giulia Barbareschi,Shruti Chandra
出处
期刊:International Journal of Social Robotics [Springer Science+Business Media]
卷期号:15 (11): 1841-1870 被引量:14
标识
DOI:10.1007/s12369-023-00968-4
摘要

Critical voices within and beyond the scientific community have pointed to a grave matter of concern regarding who is included in research and who is not. Subsequent investigations have revealed an extensive form of sampling bias across a broad range of disciplines that conduct human subjects research called "WEIRD": Western, Educated, Industrial, Rich, and Democratic. Recent work has indicated that this pattern exists within human-computer interaction (HCI) research, as well. How then does human-robot interaction (HRI) fare? And could there be other patterns of sampling bias at play, perhaps those especially relevant to this field of study? We conducted a systematic review of the premier ACM/IEEE International Conference on Human-Robot Interaction (2006-2022) to discover whether and how WEIRD HRI research is. Importantly, we expanded our purview to other factors of representation highlighted by critical work on inclusion and intersectionality as potentially underreported, overlooked, and even marginalized factors of human diversity. Findings from 827 studies across 749 papers confirm that participants in HRI research also tend to be drawn from WEIRD populations. Moreover, we find evidence of limited, obscured, and possible misrepresentation in participant sampling and reporting along key axes of diversity: sex and gender, race and ethnicity, age, sexuality and family configuration, disability, body type, ideology, and domain expertise. We discuss methodological and ethical implications for recruitment, analysis, and reporting, as well as the significance for HRI as a base of knowledge.

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