TrustVis19

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

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Browsing TrustVis19 by Subject "Visualization design and evaluation methods"

Now showing 1 - 3 of 3
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    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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    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.
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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.