Beyond Trust Building — Calibrating Trust in Visual Analytics

Wenkai Han, Hans-Jörg Schulz
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引用次数: 15

Abstract

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.
超越信任建立——在视觉分析中校准信任
信任是用户如何与可视化分析(VA)系统进行交互的基本因素。虽然在研究中已经指出了为此目的建立信任的重要性,但迄今为止,信任也可能错位的方面在很大程度上被VA忽视了。本立场文件通过将信任校准作为重点来解决这方面的问题——即,将用户的信任与VA系统的实际可信度保持一致的过程。为此,我们提出了VA背景下的信任连续体,剖析了VA系统和用户中的重要信任问题,并讨论了可以建立和校准信任的可能方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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