Exploring Human's Gender Perception and Bias toward Non-Humanoid Robots

TL;DR

This study examines how humans perceive gender and bias toward non-humanoid robots, revealing that anthropomorphic cues influence gender attribution, trustworthiness, and role perception across various robotic forms.

Contribution

It provides new insights into gender perception in non-humanoid robots and highlights the impact of design elements on user acceptance and trust.

Findings

Gender attribution occurs based on anthropomorphic features.

Design elements influence perceived roles and trustworthiness.

Non-humanoid robots are subject to gender bias similar to humanoid robots.

Abstract

In this study, we investigate the human perception of gender and bias toward non-humanoid robots. As robots increasingly integrate into various sectors beyond industry, it is essential to understand how humans engage with non-humanoid robotic forms. This research focuses on the role of anthropomorphic cues, including gender signals, in influencing human robot interaction and user acceptance of non-humanoid robots. Through three surveys, we analyze how design elements such as physical appearance, voice modulation, and behavioral attributes affect gender perception and task suitability. Our findings demonstrate that even non-humanoid robots like Spot, Mini-Cheetah, and drones are subject to gender attribution based on anthropomorphic features, affecting their perceived roles and operational trustworthiness. The results underscore the importance of balancing design elements to optimize both functional efficiency and user relatability, particularly in critical contexts.