Katie Seaborn
Tokyo Institute of Technology, Tokyo, Japan
Gender/ing guides how we view ourselves, the world around us, and each other—including non-humans. Critical voices have raised the alarm about stereotyped gendering in the design of socially embodied artificial agents like voice assistants, conversational agents, and robots. Yet, little is known about how this plays out in research and to what extent. As a first step, we critically reviewed the case of Pepper, a gender-ambiguous humanoid robot. We conducted a systematic review (n=75) involving meta-synthesis and content analysis, examining how participants and researchers gendered Pepper through stated and unstated signifiers and pronoun usage. We found that ascriptions of Pepper’s gender were inconsistent, limited, and at times discordant, with little evidence of conscious gendering and some indication of researcher influence on participant gendering. We offer six challenges driving the state of affairs and a practical framework coupled with a critical checklist for centering gender in research on artificial agents.
https://dl.acm.org/doi/abs/10.1145/3491102.3501996
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)