@conference{694,
  keywords = {uncertainty, Input visualization, probability, knowledge-assisted visual analytics},
  author = {Nikolaus Piccolotto and Fatih Öztank and Silvia Miksch and Markus Bögl},
  title = {Towards Visualization-Supported Uncertainty Elicitation},
  abstract = {<p>Expert knowledge in visual analytics (VA) informs design processes, is required to gain insights from data, and may steer computational inference of patterns and trends in the data. Many fields and industries, such as food safety or civil engineering, rely on experts’ subjective probabilities of future events and uncertain quantities to make rational and informed decisions given particular risks. For example, subjective probabilities may be put into Bayesian models as prior distributions of variables. In VA, too, experts are asked to provide (elicit) subjective probabilities, but our field has not yet developed good practices on how visual-interactive interfaces to elicit subjective uncertainties should be designed and evaluated. In an attempt to divide and conquer, this paper provides relevant research directions and opportunities after reviewing the literature on uncertainty elicitation (UE) and uncertainty visualization.</p>},
  year = {2025},
  journal = {IEEE VIS 2025},
  month = {11/2025},
  address = {Wien},
  doi = {10.34726/11663},
}