Browsing TrustVis19 by Title

Now showing items 1-6 of 6

    • Detection of Confirmation and Distinction Biases in Visual Analytics Systems 

      Nalcaci, Atilla Alpay; Girgin, Dilara; Balki, Semih; Talay, Fatih; Boz, Hasan Alp; Balcisoy, Selim (The Eurographics Association, 2019)
      Cognitive bias is a systematic error that introduces drifts and distortions in the human judgment in terms of visual decomposition in the direction of the dominant instance. It has a significant role in decision-making ...

    • Examining the Components of Trust in Map-Based Visualizations 

      Xiong, Cindy; Padilla, Lace; Grayson, Kent; Franconeri, Steven (The Eurographics Association, 2019)
      Prior research suggests that perceived transparency is often associated with perceived trust. For some data types, greater transparency in data visualization is also associated with an increase in the amount of information ...

    • Towards Supporting Interpretability of Clustering Results with Uncertainty Visualization 

      Kinkeldey, Christoph; Korjakow, Tim; Benjamin, Jesse Josua (The Eurographics Association, 2019)
      Interpretation of machine learning results is a major challenge for non-technical experts, with visualization being a common approach to support this process. For instance, interpretation of clustering results is usually ...

    • Trust in Information Visualization 

      Mayr, Eva; Hynek, Nicole; Salisu, Saminu; Windhager, Florian (The Eurographics Association, 2019)
      Trust is an important factor that mediates whether a user will rely and build on the information displayed in a visualization. Research in other fields shows that there are different mechanisms of trust building: Users ...

    • TrustVis 2019: Frontmatter 

      Kosara, Robert; Lawonn, Kai; Linsen, Lars; Smit, Noeska (The Eurographics Association, 2019)

    • Uni- and Multi-modal Uncertainty Visualization in 2D Scalar Field Ensembles 

      Gebauer, Eike; Linsen, Lars (The Eurographics Association, 2019)
      The aim of uncertainty-aware scalar field visualization is to convey the most likely case, but also the uncertainty associated with it. In scientific simulations, uncertainty can be modeled using an ensemble approach. ...