Automating the Path: An R&D Agenda for Human-Centered AI and Visualization

The emergence of generative AI, large language models, and foundation models is fundamentally reshaping computer science, and visualization and visual analytics are no exception. We present a systematic framework for understanding how human-centered AI (HCAI) can transform the visualization discipline. Our framework maps four key HCAI tool capabilities—amplify, augment, empower, and enhance—onto the four phases of visual sensemaking: view, explore, schematize, and report. For each combination, we review existing tools, envision future possibilities, identify challenges and pitfalls, and examine ethical considerations. This design space can serve as an R&D agenda for both visualization researchers and practitioners to integrate AI into their work as well as understanding how visualization can support HCAI research.

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Publication Details

Journal
IEEE Computer Graphics and Applications
Published
2025-05-01
DOI
https://doi.org/10.1109/mcg.2025.3559374
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
0.00

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article

Automating the Path: An R&D Agenda for Human-Centered AI and Visualization

Niklas Elmqvist, Clemens Nylandsted Klokmose
IEEE Computer Graphics and Applications
Explainable Artificial Intelligence (XAI)
article

Automating the Path: An R&D Agenda for Human-Centered AI and Visualization

Niklas Elmqvist, Clemens Nylandsted Klokmose
article en

Abstract

The emergence of generative AI, large language models, and foundation models is fundamentally reshaping computer science, and visualization and visual analytics are no exception. We present a systematic framework for understanding how human-centered AI (HCAI) can transform the visualization discipline. Our framework maps four key HCAI tool capabilities—amplify, augment, empower, and enhance—onto the four phases of visual sensemaking: view, explore, schematize, and report. For each combination, we review existing tools, envision future possibilities, identify challenges and pitfalls, and examine ethical considerations. This design space can serve as an R&D agenda for both visualization researchers and practitioners to integrate AI into their work as well as understanding how visualization can support HCAI research.

IEEE Computer Graphics and ApplicationsVol. 45(3)
Aarhus University (DK)
Villum Fonden
Openalex Percentile: Top 91%
Explainable Artificial Intelligence (XAI)
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