Helping Not Hurting: Applying the Stereotype Content Model and BIAS Map to Social Robotics

Hannah Mieczkowski, S. Liu, Jeffrey T. Hancock, Byron Reeves
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引用次数: 23

Abstract

This paper examines relationships between perceptions of warmth and competence, emotional responses, and behavioral tendencies in the context of social robots. Participants answered questions about these three aspects of impression formation after viewing an image of one of 342 social robots in the Stanford Social Robots Database. Results suggest that people have similar emotional and behavioral reactions to robots as they have to humans; impressions of the robots' warmth and competence predicted specific emotional responses (admiration, envy, contempt, pity) and those emotional responses predicted distinct behavioral tendencies (active facilitation, active harm, passive facilitation, passive harm). However, the predicted relationships between impressions and harmful behavioral tendencies were absent. This novel asymmetry for perceptions and intentions towards robots is deliberated in the context of the computers as social actors framework and opportunities for further research are discussed.
帮助而不是伤害:将刻板印象内容模型和偏见地图应用于社交机器人
本文研究了在社交机器人的背景下,对温暖和能力的感知、情绪反应和行为倾向之间的关系。在观看斯坦福社交机器人数据库中的342个社交机器人中的一个的图像后,参与者回答了关于印象形成的这三个方面的问题。研究结果表明,人们对机器人的情感和行为反应与对人类的相似;对机器人的热情和能力的印象预测了特定的情绪反应(钦佩、嫉妒、蔑视、怜悯),而这些情绪反应预测了不同的行为倾向(主动促进、主动伤害、被动促进、被动伤害)。然而,印象和有害行为倾向之间的预测关系是不存在的。在计算机作为社会行动者的框架和进一步研究的机会的背景下,对机器人的感知和意图的这种新颖的不对称进行了审议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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