Trust Calibration as a Function of the Evolution of Uncertainty in
Knowledge Generation: A Survey
- URL: http://arxiv.org/abs/2209.04388v1
- Date: Fri, 9 Sep 2022 16:46:37 GMT
- Title: Trust Calibration as a Function of the Evolution of Uncertainty in
Knowledge Generation: A Survey
- Authors: Joshua Boley and Maoyuan Sun
- Abstract summary: We argue that accounting for the propagation of uncertainty from data sources all the way through extraction of information is necessary to understand how user trust in a visual analytics system evolves over its lifecycle.
We sample a broad cross-section of the literature from visual analytics, human cognitive theory, and uncertainty, and attempt to synthesize a useful perspective.
- Score: 1.462008690460147
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: User trust is a crucial consideration in designing robust visual analytics
systems that can guide users to reasonably sound conclusions despite inevitable
biases and other uncertainties introduced by the human, the machine, and the
data sources which paint the canvas upon which knowledge emerges. A multitude
of factors emerge upon studied consideration which introduce considerable
complexity and exacerbate our understanding of how trust relationships evolve
in visual analytics systems, much as they do in intelligent sociotechnical
systems. A visual analytics system, however, does not by its nature provoke
exactly the same phenomena as its simpler cousins, nor are the phenomena
necessarily of the same exact kind. Regardless, both application domains
present 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 closed
to direct human input and direction during processing, are influenced by a
multitude of cognitive biases that further exacerbate an accounting of the
uncertainties that may afflict the user's confidence, and ultimately trust in
the system.
In this article we argue that accounting for the propagation of uncertainty
from data sources all the way through extraction of information and hypothesis
testing is necessary to understand how user trust in a visual analytics system
evolves over its lifecycle, and that the analyst's selection of visualization
parameters affords us a simple means to capture the interactions between
uncertainty and cognitive bias as a function of the attributes of the search
tasks the analyst executes while evaluating explanations. We sample a broad
cross-section of the literature from visual analytics, human cognitive theory,
and uncertainty, and attempt to synthesize a useful perspective.
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