What did you hear and what did you see? Understanding the transparency of facial recognition and speech recognition systems during human–robot interaction

Kun Xu, Xiaobei Chen, Fanjue Liu, Luling Huang
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Abstract

As social robots begin to assume various social roles in society, the demand for understanding how social robots work and communicate grows rapidly. While literature on explainable artificial intelligence suggests that transparency about a social robot’s working mechanism can evoke users’ positive attitudes, transparency may also have negative outcomes. This study investigates the paradoxical effects of the transparency of facial recognition technology and speech recognition technology in human–robot interactions. Based on a lab experiment and combined analyses of users’ quantitative and qualitative responses, this study suggests that the transparency of facial recognition technology in human–robot interaction increases users’ social presence, reduces privacy concerns, and enhances users’ acceptance of robots. However, exposure to both facial and speech recognition technologies revives users’ privacy worries. This study further parses users’ open-ended evaluation of the prospective application of social robots’ tracking technologies and discusses the theoretical, practical, and ethical value of the findings.
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你听到了什么,又看到了什么?了解人机交互过程中面部识别和语音识别系统的透明度
随着社交机器人开始在社会中扮演各种社会角色,人们对了解社交机器人如何工作和交流的需求迅速增长。尽管有关可解释人工智能的文献表明,社交机器人工作机制的透明化可以唤起用户的积极态度,但透明化也可能带来负面影响。本研究调查了人脸识别技术和语音识别技术的透明度在人机交互中的矛盾效应。基于实验室实验以及对用户定量和定性反应的综合分析,本研究表明,人脸识别技术在人机交互中的透明性会增加用户的社会存在感,减少对隐私的担忧,并提高用户对机器人的接受度。然而,同时接触面部识别和语音识别技术会重新引起用户对隐私的担忧。本研究进一步解析了用户对社交机器人追踪技术应用前景的开放式评价,并讨论了研究结果的理论、实践和伦理价值。
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