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Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models
V Schetinger1, S Di Bartolomeo2, M El-Assady3
1TU Wien.
Computer Graphics Forum : Journal of the European Association for Computer Graphics
|March 20, 2024
Summary
Generative AI models offer new opportunities and risks for data visualization. This study explores their potential roles across the visualization workflow, identifying key research areas.
Area of Science:
- Computer Science
- Data Visualization
- Artificial Intelligence
Background:
- Generative text-to-image models (e.g., DALL-E, MidJourney, Stable Diffusion) show significant advancements in graphical domains.
- Concerns arise regarding human agency and meaning production in art due to AI's capabilities.
- These models are poised for integration into the visualization field, necessitating proactive exploration.
Purpose of the Study:
- To investigate the potential roles of generative models within the data visualization workflow.
- To develop a framework for understanding AI's impact on visualization processes.
- To identify both opportunities and risks associated with integrating generative AI in visualization.
Main Methods:
- Constructed a framework to analyze generative models' capabilities at different visualization stages.
- Conducted semi-structured interviews with 21 experts from relevant fields.
- Synthesized expert insights to map the landscape of AI in visualization.
Main Results:
- Identified a spectrum of potential applications and challenges for generative AI in visualization.
- Highlighted areas of immediate research potential ('low-hanging fruits').
- Documented concerns and 'doomsday prophecies' regarding AI's impact on the field.
Conclusions:
- Generative AI presents both transformative opportunities and significant risks for data visualization.
- A structured approach is needed to navigate the integration of these technologies.
- Further research is crucial to harness AI's benefits while mitigating potential drawbacks.
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