Trust in Information Visualization

Mayr, Eva; Hynek, Nicole; Salisu, Saminu; Windhager, Florian

doi:10.2312/trvis.20191187

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…B8: Establish Trust in a Network Visualization (1)-Trust in visualization is a generic topic [50] and poses a potential barrier to network visualization in itself (O10). It can influence if analysts engage in a network visualization process in the first place, how findings are derived from the visualizations, and if a network visualization is deemed valid for evidence in (scientific) communication.…”

Section: Steps In Network Explorationmentioning

confidence: 99%

Understanding Barriers to Network Exploration With Visualization: A Report from the Trenches

Alkadi

Serrano

Scott-Brown

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Section: Steps In Network Explorationmentioning

confidence: 99%

Understanding Barriers to Network Exploration With Visualization: A Report from the Trenches

Alkadi

Serrano

Scott-Brown

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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“…First, there is no widely accepted definition for trust in intelligent systems, although many definitions have been proposed [72][73][74]. Second, measuring trust is very challenging because it evolves [75][76][77] and is affected by many factors [78], for example, domain expertise [75,77], visualised information and uncertainty [48,79], model accuracy [80,81], and level of transparency [82]. In addition, there is growing consensus among XAI researchers that optimising trust is not always desirable; rather, the stress should lie on appropriate trust [58] and trust calibration [83,84].…”

Section: Trust In Intelligent Systemsmentioning

confidence: 99%

Visually Explaining Uncertain Price Predictions in Agrifood: A User-Centred Case-Study

Ooge

Verbert

2022

Agriculture

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The rise of ‘big data’ in agrifood has increased the need for decision support systems that harvest the power of artificial intelligence. While many such systems have been proposed, their uptake is limited, for example because they often lack uncertainty representations and are rarely designed in a user-centred way. We present a prototypical visual decision support system that incorporates price prediction, uncertainty, and visual analytics techniques. We evaluated our prototype with 10 participants who are active in different parts of agrifood. Through semi-structured interviews and questionnaires, we collected quantitative and qualitative data about four metrics: usability, usefulness and needs, model understanding, and trust. Our results reveal that the first three metrics can directly and indirectly affect appropriate trust, and that perception differences exist between people with diverging experience levels in predictive modelling. Overall, this suggests that user-centred approaches are key for increasing uptake of visual decision support systems in agrifood.

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“…Besides the visualization design process and the context of a visualization, a main factor which adds to a visualization's trustworthiness is the underlying data [MHSW19]. Here, criteria such as accuracy, coverage, objectivity and validity can be seen as a measures that lead to a high trustworthiness of a dataset [KFW08].…”

Section: Towards Ethical and Collaborative Visualization Designmentioning

confidence: 99%

Topography of Violence: Considerations for Ethical and Collaborative Visualization Design

Ehmel

Brüggemann

Dörk

2021

Computer Graphics Forum

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Based on a collaborative visualization design process involving sensitive historical data and historiographical expertise, we investigate the relevance of ethical principles in visualization design. While fundamental ethical norms like truthfulness and accuracy are already well‐described and common goals in visualization design, datasets that are accompanied by specific ethical concerns need to be processed and visualized with an additional level of carefulness and thought. There has been little research on adequate visualization design incorporating such considerations. To address this gap we present insights from Topography of Violence, a visualization project with the Jewish Museum Berlin that focuses on a dataset of more than 4,500 acts of violence against Jews in Germany between 1930 and 1938. Drawing from the joint project, we develop an approach to the visualization of sensitive data, which features both conceptual and procedural considerations for visualization design. Our findings provide value for both visualization researchers and practitioners by highlighting challenges and opportunities for ethical data visualization.

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Trust in Information Visualization

Cited by 13 publications

References 18 publications

Understanding Barriers to Network Exploration With Visualization: A Report from the Trenches

Understanding Barriers to Network Exploration With Visualization: A Report from the Trenches

Visually Explaining Uncertain Price Predictions in Agrifood: A User-Centred Case-Study

Topography of Violence: Considerations for Ethical and Collaborative Visualization Design

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