Zobrazeno 1 - 10
of 778
pro vyhledávání: '"MÖLLER, TORSTEN"'
XAI research often focuses on settings where people learn about and assess algorithmic systems individually. However, as more public AI systems are deployed, it becomes essential for XAI to facilitate collective understanding and deliberation. We con
Externí odkaz:
http://arxiv.org/abs/2411.11449
The ability to read, interpret, and critique data visualizations has mainly been assessed using data visualization tasks like value retrieval. Although evidence on different facets of Visual Data Literacy (VDL) is well established in visualization re
Externí odkaz:
http://arxiv.org/abs/2410.23807
Visual Parameter Space Analysis (VPSA) enables domain scientists to explore input-output relationships of computational models. Existing VPSA applications often feature multi-view visualizations designed by visualization experts for a specific scenar
Externí odkaz:
http://arxiv.org/abs/2409.07105
The analysis of complex high-dimensional data is a common task in many domains, resulting in bespoke visual exploration tools. Expectations and practices of domain experts as users do not always align with visualization theory. In this paper, we repo
Externí odkaz:
http://arxiv.org/abs/2404.03965
Autor:
Chen, Jian, Isenberg, Petra, Laramee, Robert S., Isenberg, Tobias, Sedlmair, Michael, Moeller, Torsten, Li, Rui
We present and discuss the results of a qualitative analysis of visual representations from images. We labeled each image's essential stimuli, the removal of which would render a visualization uninterpretable. As a result, we derive a typology of 10
Externí odkaz:
http://arxiv.org/abs/2403.05594
Publikováno v:
International Journal of Human-Computer Studies, Volume 193, 2025, 103380, ISSN 1071-5819
Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novice
Externí odkaz:
http://arxiv.org/abs/2401.13324
Vast amounts of (open) data are increasingly used to make arguments about crisis topics such as climate change and global pandemics. Data visualizations are central to bringing these viewpoints to broader publics. However, visualizations often concea
Externí odkaz:
http://arxiv.org/abs/2310.18011
In contrast to objectively measurable aspects (such as accuracy, reading speed, or memorability), the subjective experience of visualizations has only recently gained importance, and we have less experience how to measure it. We explore how subjectiv
Externí odkaz:
http://arxiv.org/abs/2310.13713
Charts are used to communicate data visually, but designing an effective chart that a broad set of people can understand is challenging. Usually, we do not know whether a chart's intended message aligns with the message readers perceive. In this mixe
Externí odkaz:
http://arxiv.org/abs/2310.05752
Casual data visualizations play a vital role in communicating data to lay audiences. Despite this, little is known about how data visualization practitioners make design decisions based on their envisioned target audiences using different media chann
Externí odkaz:
http://arxiv.org/abs/2310.01935