TrustVis19

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https://diglib.eg.org/handle/10.2312/2632783

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Trust in Information Visualization
(The Eurographics Association, 2019) Mayr, Eva; Hynek, Nicole; Salisu, Saminu; Windhager, Florian; Kosara, Robert and Lawonn, Kai and Linsen, Lars and Smit, Noeska
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 might elaborate the information deeply and gain a good understanding of the uncertainties in the data and quality of the information. But they might also use superficial cues as indicators for trust. Which processes are activated depends on the trustworthiness on the side of the visualization and on the trust perception by the users. We lay out challenges for future research to further improve our understanding of trust in information visualization.
Uni- and Multi-modal Uncertainty Visualization in 2D Scalar Field Ensembles
(The Eurographics Association, 2019) Gebauer, Eike; Linsen, Lars; Kosara, Robert and Lawonn, Kai and Linsen, Lars and Smit, Noeska
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. Statistics are then drawn from the ensemble outcome to compute the most likely case and its uncertainty. However, the statistical distributions do not necessarily need to be uni-modal. We present an approach to visualize uncertain 2D scalar fields that extends existing uni-modal distributions based on colored heightfields and 2D glyphs to multi-modal ones. We compare the approaches by conducting user experiments for both the uni- and multi-modal case.
Towards Supporting Interpretability of Clustering Results with Uncertainty Visualization
(The Eurographics Association, 2019) Kinkeldey, Christoph; Korjakow, Tim; Benjamin, Jesse Josua; Kosara, Robert and Lawonn, Kai and Linsen, Lars and Smit, Noeska
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 based on scatterplots that provide information about cluster characteristics implicitly through the relative location of objects. However, the locations and distances tend to be distorted because of artifacts stemming from dimensionality reduction. This makes interpretation of clusters difficult and may lead to distrust in the system. Most existing approaches that counter this drawback explain the distances in the scatterplot (e.g., error visualization) to foster the interpretability of implicit information. Instead, we suggest explicit visualization of the uncertainty related to the information needed for interpretation, specifically the uncertain membership of each object to its cluster. In our approach, we place objects on a grid, and add a continuous ''topography'' in the background, expressing the distribution of uncertainty over all clusters. We motivate our approach from a use case in which we visualize research projects, clustered by topics extracted from scientific abstracts. We hypothesize that uncertainty visualization can increase trust in the system, which we specify as an emergent property of interaction with an interpretable system. We present a first prototype and outline possible procedures for evaluating if and how the uncertainty visualization approach affects interpretability and trust.
TrustVis 2019: Frontmatter
(The Eurographics Association, 2019) Kosara, Robert; Lawonn, Kai; Linsen, Lars; Smit, Noeska; Kosara, Robert and Lawonn, Kai and Linsen, Lars and Smit, Noeska
Examining the Components of Trust in Map-Based Visualizations
(The Eurographics Association, 2019) Xiong, Cindy; Padilla, Lace; Grayson, Kent; Franconeri, Steven; Kosara, Robert and Lawonn, Kai and Linsen, Lars and Smit, Noeska
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 depicted. Based on prior work in economics and political science that has identified four dimensions of transparency, we examined the influence of accuracy, clarity, amount of disclosure, and thoroughness on a decision task where participants relied on map-based visualizations with varying complexity to solve a crisis. The results of our preliminary analysis suggest that perceived clarity, amount of disclosure. and thoroughness significantly predicted individuals' selection of a Google Maps-like application with either less information or more information. Trust and perceived accuracy did not significantly predict which navigation application visualization participants decided to use (i.e., one with more information or less information). Further, our preliminary results suggest that an individual's ratings of accuracy and disclosure of a visualization predicted their ratings of the trustworthiness of that visualization. We discuss the implications of a possible dissociation between trust and decision tasks on visualization evaluation. In future work, we aim to examine the influence of the amount of information shown in a visualization on ratings of trust and determine the generalizability of our preliminary findings to different task types and visualization approaches.
Detection of Confirmation and Distinction Biases in Visual Analytics Systems
(The Eurographics Association, 2019) Nalcaci, Atilla Alpay; Girgin, Dilara; Balki, Semih; Talay, Fatih; Boz, Hasan Alp; Balcisoy, Selim; Kosara, Robert and Lawonn, Kai and Linsen, Lars and Smit, Noeska
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 process by means of evaluation of data visualizations. This paper elaborates on the experimental depiction of two cognitive bias types, namely Distinction Bias and Confirmation Bias, through the examination of cognate visual experimentations. The main goal of this implementation is to indicate the existence of cognitive bias in visual analytics systems through the adjustment of data visualization and crowdsourcing in terms of confirmation and distinction biases. Two distinct surveys that include biased and unbiased data visualizations which are related to a given data set were established in order to detect and measure the level of existence of introduced bias types. Practice of crowdsourcing which is provided by Amazon Mechanical Turk have been used for experimentation purposes through prepared surveys. Results statistically indicate that both distinction and confirmation biases has substantial effect and prominent significance on decision-making process.

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