Trust-Building – Valcri https://valcri.org VALCRI is a European Union project Tue, 01 Dec 2015 18:53:16 +0000 en-US hourly 1 https://wordpress.org/?v=5.2.2 VAPD – A Visionary System for Uncertainty Aware Decision Making in Crime Analysis https://euprojectvalcri.org/publications/vapd-a-visionary-system-for-uncertainty-aware-decision-making-in-crime-analysis/ Fri, 30 Oct 2015 16:55:00 +0000 https://valcri.demo.steellondon.com/?p=1203 ...]]> F. Stoffel, D. Sacha, G. Ellis, and D. A. Keim, “VAPD – A Visionary System for Uncertainty Aware Decision Making in Crime Analysis,” in Symposium on Visualization for Decision Making Under Uncertainty at IEEE VIS 2015, 2015.

Abstract:
In this paper we describe a visionary system, VAPD, which supports crime analysts in uncertainty aware decision making in use of comparative case analysis. In this scenario, it is crucial for crime analysts to get an accurate estimate of uncertainties included in their data as well as those caused through data transformations and mappings, thus supporting analysts in calibrating their trust in the pieces of evidence gained through data analytics. VAPD consists of one data processing and three visualisation components that adopt a set of guidelines for handling uncertainties. The system focuses on conveying an accurate estimate of these uncertainties on processes and uncertainties that occur within its natural language processing components. Text data analysis is ambiguous and error prone, but is nevertheless an important part of the data analysis. Through its innovative handling of uncertainties, VAPD enables
transparent and reliable decisions based on uncertainty-aware visual analytics.

keywords —Uncertainty, Provenance, Trust-Building, Crime Analysis.

]]> The Role of Uncertainty, Awareness, and Trust in Visual Analytics https://euprojectvalcri.org/publications/the-role-of-uncertainty-awareness-and-trust-in-visual-analytics/ Mon, 19 Oct 2015 16:52:23 +0000 https://valcri.demo.steellondon.com/?p=1195 ...]]> D. Sacha, H. Senaratne, B. C. Kwon, G. Ellis, and D. A. Keim, “The Role of Uncertainty, Awareness, and Trust in Visual Analytics,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 240–249, Jan. 2016.

Abstract:

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.

keywords —Visual Analytics, Knowledge Generation, Uncertainty Measures and Propagation, Trust Building, Human Factors

URL

]]>