@inproceedings{60d464e028144502af05869317757289,
title = "People{\textquoteright}s Perceptions of Gendered Robots Performing Gender Stereotypical Tasks",
abstract = "HRI research shows that people prefer robot appearances that fit their given task but also identify stereotypical social perceptions of robots caused by a gendered appearance. This study investigates stereotyping effects of both robot genderdness (male vs. female) and assigned task (analytical vs. social) on people{\textquoteright}s evaluations of trust, social perception, and humanness in an online vignette study (n = 89) with a between subject{\textquoteright}s design. People deem robots more competent and receive higher capacity trust when they perform analytical tasks compared to social tasks, independent of the robot{\textquoteright}s gender. An observed trend in the data implies a tendency to dehumanize robots as an effect of their gendered appearance, sometimes as an interaction effect with performed task when this contradicts gender stereotypical expectations. Our results stress further exploration of robot gender by varying gender cues and considering alternative task descriptions, as well as highlight potential new directions in studying human misconduct towards robots.",
keywords = "Social robots, Gender stereotypes, Social perception, Dehumanization, Trust",
author = "{de Graaf}, Maartje",
year = "2021",
doi = "10.1007/978-3-030-90525-5_3",
language = "English",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "24--35",
editor = "Haizhou Li and {Sam Ge}, Shuzhi and Yan Wu and Agnieszka Wykowska and Hongsheng He and Xiaorui Liu and Dongyu Li and Jairo Perez-Osorio",
booktitle = "Social Robotics",
}