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Do You Trust What You See? Toward A Multidimensional Measure of Trust in Visualization

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arxiv 2308.04727 v1 pith:J4GMK4W7 submitted 2023-08-09 cs.HC

classification cs.HC
keywords trustfactorsvisualizationdesigncredibilityfivereliabilityvisualizations
verification ladder T0 review T1 audit T2 compute T3 formal
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Few concepts are as ubiquitous in computational fields as trust. However, in the case of information visualization, there are several unique and complex challenges, chief among them: defining and measuring trust. In this paper, we investigate the factors that influence trust in visualizations. We draw on the literature to identify five factors likely to affect trust: credibility, clarity, reliability, familiarity, and confidence. We then conduct two studies investigating these factors' relationship with visualization design features. In the first study, participants' credibility, understanding, and reliability ratings depended on the visualization design and its source. In the second study, we find these factors also align with subjective trust rankings. Our findings suggest that these five factors are important considerations for the design of trustworthy visualizations.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. When LLM Rationales Become User-Facing: Effects on Trust Perception, Decision-Making, and Gaze Behaviors

    cs.HC 2026-06 unverdicted novelty 5.0 of 10

    Two linked user studies find that LLM rationale correctness and certainty framing affect trust and decision confidence while presentation format does not, and incorrect rationales increase gaze attention and pupil size.

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