测量智能机器人的透明度
Measuring Transparency in Intelligent Robots
AI总结：
本文提出首个多语言标准化机器人透明度量表TOROS，通过三阶段实证研究将其构建为包含可解释性、可预测性和不可读性三个维度的26项测量工具，为人机交互研究提供了可比较的基准。
摘要：
随着机器人日益融入我们的日常生活，使其具备透明度的需求从未像现在这样紧迫。然而，尽管透明度在人机交互中十分重要，迄今为止仍缺乏一种标准化的机器人透明度测量工具。本文通过提出首个用于测量机器人系统感知透明度的综合量表来填补这一空白，该量表提供英语、德语和意大利语三种语言版本。我们的方法将透明度概念化为一个多维构念，涵盖可解释性（explainability）、可读性（legibility）、可预测性（predictability）和元理解（meta-understanding）。所提出的量表经过了一个严格的三阶段过程，共有1,223名参与者参与。首先，我们生成了量表的条目；其次，我们进行了探索性因子分析；第三，通过验证性因子分析验证了新开发的TOROS量表的因子结构。最终量表包含26个条目，由三个因子组成：不可读性（Illegibility）、可解释性（Explainability）和可预测性（Predictability）。TOROS在三个跨国样本中表现出较高的跨语言信度、因子间相关性、模型拟合度、内部一致性和收敛效度。这一经过实证验证的工具能够评估机器人透明度，并有助于对这一复杂构念的理论理解。通过提供标准化测量工具，我们促进了人机交互领域中一致且可比较的研究，TOROS可作为基准使用。
英文摘要：
As robots become increasingly integrated into our daily lives, the need to make them transparent has never been more critical. Yet, despite its importance in human-robot interaction, a standardized measure of robot transparency has been missing until now. This paper addresses this gap by presenting the first comprehensive scale to measure perceived transparency in robotic systems, available in English, German, and Italian languages. Our approach conceptualizes transparency as a multidimensional construct, encompassing explainability, legibility, predictability, and meta-understanding. The proposed scale was a product of a rigorous three-stage process involving 1,223 participants. Firstly, we generated the items of our scale, secondly, we conducted an exploratory factor analysis, and thirdly, a confirmatory factor analysis served to validate the factor structure of the newly developed TOROS scale. The final scale encompasses 26 items and comprises three factors: Illegibility, Explainability, and Predictability. TOROS demonstrates high cross-linguistic reliability, inter-factor correlation, model fit, internal consistency, and convergent validity across the three cross-national samples. This empirically validated tool enables the assessment of robot transparency and contributes to the theoretical understanding of this complex construct. By offering a standardized measure, we facilitate consistent and comparable research in human-robot interaction in which TOROS can serve as a benchmark.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
