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


Visual analytics supports humans in generating information from massive and typically complex datasets. Evidence is collected, collated and cross-linked with our existing data. In the method, a myriad of analytical and visualisation techniques are employed to come up with a visual illustration of the info. These often introduce their own uncertainties, additionally to the ones inherent in the information, and these propagated and compounded uncertainties can lead to impaired call 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 data generation model for visual analytics is employed to produce a terminology and framework to discuss the results of those aspects in knowledge construction and though examples, machine uncertainty is compared to human trust measures with provenance. Furthermore, tips for the look of uncertainty-aware systems are presented that may aid the user in higher decision creating.

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