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

2025-04-10

AI Summary

This work addresses the challenge of deeply integrating Human-Centered Artificial Intelligence (HCAI) with visualization and visual analytics. We propose the first two-dimensional design space that systematically maps HCAI’s four core capabilities—amplify, augment, empower, and elevate—to the four stages of visual cognition: observe, explore, model, and report. Our framework guides bidirectional integration of AI and visualization, establishing two novel paradigms: “visualization for AI explainability” and “AI-enhanced human–machine collaboration.” Methodologically, we synthesize generative AI, large language models, foundation models, and interactive visualization techniques, while emphasizing human-centered evaluation, ethical alignment, and traceable design. The outcome is an actionable research and development roadmap that enables the visualization community to systematically incorporate HCAI principles—and positions visualization as a core enabling technology for enhancing HCAI’s trustworthiness and usability.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Visual Reasoning & Symbolic Representations
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The emergence of generative AI, large language models (LLMs), 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.

Problem

• Framework for HCAI transforming visualization discipline
• Mapping HCAI capabilities to visual sensemaking phases
• R&D agenda for AI integration in visualization

Innovation

• Systematic framework for HCAI in visualization
• Mapping HCAI capabilities to visual sensemaking phases
• R&D agenda for AI-integrated visualization research