Beyond Trust Building - Calibrating Trust in Visual Analytics

被引：12
作者：
Han, Wenkai [1 ]
Schulz, Hans-Jorg [1 ]
机构：
[1] Aarhus Univ, Aarhus, Denmark
来源：
2020 IEEE WORKSHOP ON TRUST AND EXPERTISE IN VISUAL ANALYTICS (TREX 2020) | 2020年
关键词：
Human-centered computing; Visualization; Visualization theory; concepts and paradigms; HCI theory; concepts and models; Visualization design and evaluation methods; VISUALIZATIONS; UNCERTAINTY; AUTOMATION; DESIGN; RISK;
D O I：
10.1109/TREX51495.2020.00006
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Trust is a fundamental factor in how users engage in interactions with Visual Analytics (VA) systems. While the importance of building trust to this end has been pointed out in research, the aspect that trust can also be misplaced is largely ignored in VA so far. This position paper addresses this aspect by putting trust calibration in focus - i.e., the process of aligning the user's trust with the actual trustworthiness of the VA system. To this end, we present the trust continuum in the context of VA, dissect important trust issues in both VA systems and users, as well as discuss possible approaches that can build and calibrate trust.
引用
收藏
页码：9 / 15
页数：7
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