May (A)I Beautify Your Visualization? Expert Judgments of Acceptable Aesthetic Alterations
2026-06-30 • Human-Computer Interaction
Human-Computer InteractionGraphics
AI summaryⓘ
The authors studied how changes made to 3D visualizations of natural data affect people's acceptance of those changes. They surveyed experts about different types of edits, like adjusting lighting or fixing data errors, done by humans or AI. They found that whether a change is acceptable depends more on what the change means than on how it is done. Also, people generally trust human-made changes more than AI-made ones, even if the changes are the same. The authors suggest visualization designers and AI tools should be careful to follow rules that respect these differences.
3D visualizationnatural phenomenadata transformationpresentation-level adjustmentsdata-level modificationsAI in visualizationhuman-computer interactionexpert surveyvisual designtrust in AI
Authors
Kalina Borkiewicz, Jixian Li, Joshua A. Levine, Katherine E. Isaacs
Abstract
In 3D visualizations of natural phenomena, improving aesthetics can provide measurable benefits, but often involves transformations that affect how the data is perceived. As a growing range of tools - including AI-based methods - make visual design and modification more accessible, it is increasingly important to understand trade offs and concerns when making these changes. We conducted an expert survey (N=95) with visualization researchers, practitioners, and domain scientists, investigating reactions to fifteen alterations spanning presentation-level adjustments (e.g., lighting, camera position) and data-level modifications (e.g., removing errors, filling gaps), applied by both humans and AI systems. Results show differences in perceived acceptability are driven by the transformation's meaning, regardless of whether it operates at the presentation or data level. Additionally, certain modifications were consistently judged as more permissible than others regardless of human or AI authorship. While this relative ordering remains largely stable, AI-generated transformations are consistently rated as less acceptable than identical human-produced changes. These results reveal a distinction between more permissible and more sensitive alterations, and suggest the need for both designers and AI-assisted visualization tools to incorporate constraints and guardrails that reflect these differences.