Exploring Human's Gender Perception and Bias toward Non-Humanoid Robots
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| author | Ramezani, Mahya Sanchez-Lopez, Jose Luis |
| author_facet | Ramezani, Mahya Sanchez-Lopez, Jose Luis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_12001 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Exploring Human's Gender Perception and Bias toward Non-Humanoid Robots Ramezani, Mahya Sanchez-Lopez, Jose Luis Robotics Human-Computer Interaction 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. |
| title | Exploring Human's Gender Perception and Bias toward Non-Humanoid Robots |
| topic | Robotics Human-Computer Interaction |
| url | https://arxiv.org/abs/2309.12001 |