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User trust is a crucial consideration in designing robust visual analyticssystems that can guide users to reasonably sound conclusions despite inevitablebiases and other uncertainties introduced by the human, the machine, and thedata sources which paint the canvas upon which knowledge emerges. A multitudeof factors emerge upon studied consideration which introduce considerablecomplexity and exacerbate our understanding of how trust relationships evolvein visual analytics systems, much as they do in intelligent sociotechnicalsystems. A visual analytics system, however, does not by its nature provokeexactly the same phenomena as its simpler cousins, nor are the phenomenanecessarily of the same exact kind. Regardless, both application domainspresent the same root causes from which the need for trustworthiness arises:Uncertainty and the assumption of risk. In addition, visual analytics systems,even more than the intelligent systems which (traditionally) tend to be closedto direct human input and direction during processing, are influenced by amultitude of cognitive biases that further exacerbate an accounting of theuncertainties that may afflict the user's confidence, and ultimately trust inthe system.  In this article we argue that accounting for the propagation of uncertaintyfrom data sources all the way through extraction of information and hypothesistesting is necessary to understand how user trust in a visual analytics systemevolves over its lifecycle, and that the analyst's selection of visualizationparameters affords us a simple means to capture the interactions betweenuncertainty and cognitive bias as a function of the attributes of the searchtasks the analyst executes while evaluating explanations. We sample a broadcross-section of the literature from visual analytics, human cognitive theory,and uncertainty, and attempt to synthesize a useful perspective.

Trust Calibration as a Function of the Evolution of Uncertainty in Knowledge Generation: A Survey