The Role of Uncertainty, Awareness, and Trust in Visual Analytics

• Published in: IEEE transactions on visualization and computer graphics Vol. 22; no. 1; pp. 240 - 249
• Main Authors: Sacha, Dominik, Senaratne, Hansi, Bum Chul Kwon, Ellis, Geoffrey, Keim, Daniel A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Analytics; Awareness; Buildings; Computer Graphics; Crosslinking; Data models; Data visualization; Decision making; Ergonomics; Human Factors; Humans; Knowledge Generation; Mathematical analysis; Trust - psychology; Trust Building; Trustworthiness; Uncertainty; Uncertainty Measures and Propagation; Visual analytics; Knowledge Generation; Trust Building; Visual Analytics; Uncertainty Measures and Propagation; Human Factors
• ISSN: 1077-2626; 1941-0506
• 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.