How Do We Measure Trust in Visual Data Communication?
How Do We Measure Trust in Visual Data Communication?
复制标题
我们如何衡量视觉数据通信中的信任？
DOI：
10.1109/beliv57783.2022.00014
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Cindy Xiong
中科院分区：
文献类型：
--
作者：
Hamza Elhamdadi;Aimen Gaba;Yea;Cindy Xiong
信任是可视化设计者和读者之间有效的可视化数据通信的基础。虽然个人经验和偏好会影响读者对可视化的信任，但可视化设计师可以利用设计技术来创建唤起“校准信任”的可视化，读者在批判性地评估所呈现的信息后到达。为了系统地了解是什么驱使读者参与“校准信任”，我们必须首先装备自己可靠和有效的方法来测量信任。计算机科学和数据可视化研究人员尚未就信任定义或度量达成共识，这对于在人-数据交互中构建全面的信任模型至关重要。另一方面，社会科学家和行为经济学家已经开发和完善了可以衡量广义信任和人际信任的指标，可视化社区可以参考，修改和适应我们的需求。在本文中，我们收集了现有的方法来评估其他学科的信任，并讨论了我们如何使用它们来衡量，定义和建模数据可视化研究中的信任。具体来说，我们讨论了社会科学的定量调查，行为经济学的信任游戏，通过测量信念更新来测量信任，通过感知方法来测量信任。我们评估这些方法的潜在问题，并考虑如何将它们系统地应用于可视化研究。
Trust is fundamental to effective visual data communication between the visualization designer and the reader. Although personal experience and preference influence readers’ trust in visualizations, visualization designers can leverage design techniques to create visualizations that evoke a "calibrated trust," at which readers arrive after critically evaluating the information presented. To systematically understand what drives readers to engage in "calibrated trust," we must first equip ourselves with reliable and valid methods for measuring trust. Computer science and data visualization researchers have not yet reached a consensus on a trust definition or metric, which are essential to building a comprehensive trust model in human-data interaction. On the other hand, social scientists and behavioral economists have developed and perfected metrics that can measure generalized and interpersonal trust, which the visualization community can reference, modify, and adapt for our needs. In this paper, we gather existing methods for evaluating trust from other disciplines and discuss how we might use them to measure, define, and model trust in data visualization research. Specifically, we discuss quantitative surveys from social sciences, trust games from behavioral economics, measuring trust through measuring belief updating, and measuring trust through perceptual methods. We assess the potential issues with these methods and consider how we can systematically apply them to visualization research.
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DOI：
10.1109/tvcg.2020.3030335
发表时间：
2021-02-01
影响因子：
5.2
作者：
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DOI：
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DOI：
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发表时间：
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期刊：
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影响因子：
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DOI：
10.1145/3472749.3474740
发表时间：
2021
期刊：
UIST '21: The 34th Annual ACM Symposium on User Interface Software and Technology
影响因子：
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作者：
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影响因子：
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作者：
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