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<p>As Artificial Intelligence (AI) is increasingly employed in robots and makes interaction with robots more intricate, humans’ need for transparency becomes predominant; however, current research demonstrates debates regarding the form, degree, and implementation of transparency. The industrial and military robotics sectors highlighted the positive role of transparency in improving user experiences. In contrast, medical and service robotics usually discussed the negative impact of transparency in diminishing users’ evaluations of robots and escalating their cognitive workloads. The proposed meta-analysis, drawing from 644 publications and distilling 25 studies, sought to comprehensively comprehend the role of transparency in robotics, specifically, its impact on user trust, perception, and workload. These findings suggest that excessive transparency may undermine the trust in humanoid service robots. Subtle variations in the experimental design and nature of online platforms could influence the outcomes. The focus lies on methodological recommendations, improving existing transparency models to enable customization of transparency levels based on the practical application contexts of robots.</p>

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How Transparency Shapes the Quality of Human-Robot Interaction: An Examination of Trust, Perception, and Workload

  • 期刊论文

  • 发表日期：

    2025年5月13日

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  • Min Cai,
  • Qixuan Jin,
  • Jia Zhou,
  • Xinggang Luo

摘要

As Artificial Intelligence (AI) is increasingly employed in robots and makes interaction with robots more intricate, humans’ need for transparency becomes predominant; however, current research demonstrates debates regarding the form, degree, and implementation of transparency. The industrial and military robotics sectors highlighted the positive role of transparency in improving user experiences. In contrast, medical and service robotics usually discussed the negative impact of transparency in diminishing users’ evaluations of robots and escalating their cognitive workloads. The proposed meta-analysis, drawing from 644 publications and distilling 25 studies, sought to comprehensively comprehend the role of transparency in robotics, specifically, its impact on user trust, perception, and workload. These findings suggest that excessive transparency may undermine the trust in humanoid service robots. Subtle variations in the experimental design and nature of online platforms could influence the outcomes. The focus lies on methodological recommendations, improving existing transparency models to enable customization of transparency levels based on the practical application contexts of robots.