activity
20182024
most citedNot As Easy As You Think -- Experiences and Lessons Learnt from Trying to Create a Bottom-Up Visualization Image Typology

4 citations · 4 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.HC2024

Better Together? The Role of Explanations in Supporting Novices in Individual and Collective Deliberations about AI

Timothée Schmude, Laura Koesten, Torsten Möller +1

Deploying AI systems in public institutions can have far-reaching consequences for many people, making it a matter of public interest. Providing opportunities for stakeholders to c…

cs.HC2024

An Image-based Typology for Visualization

Jian Chen, Petra Isenberg, Robert S. Laramee +4

We present and discuss the results of a qualitative analysis of visualization images to derive an image-based typology of visualizations. For each image, we seek to identify its ma…

cs.HC2024

Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions

Timothée Schmude, Laura Koesten, Torsten Möller +1

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 th…

cs.HC2023

The Gulf of Interpretation: From Chart to Message and Back Again

Christian Knoll, Torsten Möller, Kathleen Gregory +1

Charts are used to communicate data visually, but often, we do not know whether a chart's intended message aligns with the message readers perceive. In this mixed-methods study, we…

cs.HC2023

Practitioners' Perspectives on Designing Data Visualizations for the General Public

Regina Schuster, Kathleen Gregory, Torsten Möller +1

Public-facing data visualizations can play a vital role in making complex information clear and engaging, thereby encouraging informed public discourse and participation. However,…