activity
20242026
most citedBetter Together? The Role of Explanations in Supporting Novices in Individual and Collective Deliberations about AI

2 citations · 2 across the 3 of their papers we have counts for

collaborators

10 papers

cs.HC20262 cited

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.HC2026

Structured Visualization Design Knowledge for Grounding Generative Reasoning and Situated Feedback

Péter Ferenc Gyarmati, Dominik Moritz, Torsten Möller +1

Automated visualization design navigates a tension between symbolic systems and generative models. Constraint solvers enforce structural and perceptual validity, but the rules they…

cs.HC2026

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,…

cs.HC2025

PLUTO: A Public Value Assessment Tool

Laura Koesten, Péter Ferenc Gyarmati, Connor Hogan +4

We present PLUTO (Public VaLUe Assessment TOol), a framework for assessing the public value of specific instances of data use. Grounded in the concept of data solidarity, PLUTO aim…

cs.HC2025

A Composable Agentic System for Automated Visual Data Reporting

Péter Ferenc Gyarmati, Dominik Moritz, Torsten Möller +1

To address the brittleness of monolithic AI agents, our prototype for automated visual data reporting explores a Human-AI Partnership model. Its hybrid, multi-agent architecture st…

cs.HC2025

Do Vision-Language Models See Visualizations Like Humans? Alignment in Chart Categorization

Péter Ferenc Gyarmati, Manfred Klaffenböck, Laura Koesten +1

Vision-language models (VLMs) hold promise for enhancing visualization tools, but effective human-AI collaboration hinges on a shared perceptual understanding of visual content. Pr…