May (A)I Beautify Your Visualization? Expert Judgments of Acceptable Aesthetic Alterations
Authors
Kalina Borkiewicz (University of Utah), Jixian Li (University of Utah), Joshua A Levine (University of Arizona), Katherine E. Isaacs (The University of Utah)
Presentation
- Session
- Data, Meet Human: Vis That Cares
- Time
- Wednesday, Nov 11, 10:00 – 10:09 (US/Eastern) · session 10:00 – 11:30
- Location
- Hall America north
Links
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Keywords
Cinematic scientific visualization, science communication, visualization aesthetics, generative AI
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.
For Practitioners
This paper is primarily relevant to two groups of practitioners. First, developers of AI-assisted visualization tools can use these findings to prioritize transformations that are broadly viewed as acceptable, identify higher-risk modifications that may warrant stronger controls or explainability, and inform the design of guardrails for AI-assisted editing. Second, practitioners who create visualizations for public communication, including scientific visualization designers, science communicators, and visualization practitioners, can use the findings to better understand which aesthetic or narrative alterations are generally considered acceptable by experts and which types of modifications require greater caution, justification, or domain expert validation.