Sacha Dominik, Senaratne Hansi, Kwon Bum Chul, Ellis Geoffrey, Keim Daniel A
IEEE Trans Vis Comput Graph. 2016 Jan;22(1):240-9. doi: 10.1109/TVCG.2015.2467591.
Visual analytics supports humans in generating knowledge from large and often complex datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the process, a myriad of analytical and visualisation techniques are employed to generate a visual representation of the data. These often introduce their own uncertainties, in addition to the ones inherent in the data, and these propagated and compounded uncertainties can result in impaired decision making. The user's confidence or trust in the results depends on the extent of user's awareness of the underlying uncertainties generated on the system side. This paper unpacks the uncertainties that propagate through visual analytics systems, illustrates how human's perceptual and cognitive biases influence the user's awareness of such uncertainties, and how this affects the user's trust building. The knowledge generation model for visual analytics is used to provide a terminology and framework to discuss the consequences of these aspects in knowledge construction and though examples, machine uncertainty is compared to human trust measures with provenance. Furthermore, guidelines for the design of uncertainty-aware systems are presented that can aid the user in better decision making.
可视化分析支持人们从大量且通常复杂的数据集中生成知识。证据被收集、整理并与我们现有的知识进行交叉关联。在此过程中，会运用大量的分析和可视化技术来生成数据的可视化表示。除了数据本身固有的不确定性之外，这些技术往往还会引入它们自己的不确定性，而这些传播和复合的不确定性可能导致决策受损。用户对结果的信心或信任程度取决于用户对系统端产生的潜在不确定性的了解程度。本文剖析了通过可视化分析系统传播的不确定性，说明了人类的感知和认知偏差如何影响用户对这些不确定性的认识，以及这如何影响用户的信任建立。可视化分析的知识生成模型用于提供术语和框架，以讨论这些方面在知识构建中的后果，并通过示例将机器不确定性与具有出处的人类信任度量进行比较。此外还提出了不确定性感知系统的设计指南，以帮助用户做出更好的决策。
