The Shape of Our Bias

The present study was aimed at determining the age and gender distribution of the humanoid robots in the ABOT dataset, and providing a systematic data-driven formalization of the process of age and gender categorization of humanoid robots. We involved 153 participants in an online study and asked them to rate the humanoid robots in the ABOT dataset in terms of perceived age, femininity, masculinity, and gender neutrality. Our analyses disclosed that most of the robots in the ABOT dataset were perceived as young adults, and the vast majority of them were attributed a neutral or masculine gender. By merging our data with the data in the ABOT dataset, we discovered that humanlikeness is crucial to elicit social categorization. Moreover, we found out that body manipulators (e.g., legs, torso) guide the attribution of masculinity, surface look features (e.g., eyelashes, apparel) the attribution of femininity, and that robots without facial features (e.g., head, eyes) are perceived as older. Finally, yet importantly, we unveiled that men tend to attribute lower age scores and higher femininity ratings to humanoid robots than women. Our work provides evidence of an existing underlying bias in the design of humanoid robots that needs to be addressed: the under-representation of feminine robots and lack of representation of androgynous ones. We make the results of this study publicly available to the HRI community by attaching the dataset we collected to the present paper and creating a dedicated website.