| Title | Beyond Trust Building — Calibrating Trust in Visual Analytics |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Han, W., Schulz, H.-J. |
| Conference Name | 2020 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) |
| Keywords | Buildings, calibration, composability, Computing Theory, Computing Theory and Trust, concepts and models, concepts and paradigms, Data visualization, HCI theory, Human Behavior, human factors, human trust, human-centered computing, Law, pubcrawl, Resiliency, Task Analysis, Trust, Uncertainty, visual analytics, visualization, Visualization design and evaluation methods, Visualization theory |
| Abstract | Trust is a fundamental factor in how users engage in interactions with Visual Analytics (VA) systems. While the importance of building trust to this end has been pointed out in research, the aspect that trust can also be misplaced is largely ignored in VA so far. This position paper addresses this aspect by putting trust calibration in focus – i.e., the process of aligning the user’s trust with the actual trustworthiness of the VA system. To this end, we present the trust continuum in the context of VA, dissect important trust issues in both VA systems and users, as well as discuss possible approaches that can build and calibrate trust. |
| DOI | 10.1109/TREX51495.2020.00006 |
| Citation Key | han_beyond_2020 |
Beyond Trust Building — Calibrating Trust in Visual Analytics