Visual Analytics Challenges and Trends in the Age of AI: The BigVis Community Perspective

2025-04-30

AI Summary

This study addresses core challenges in human-data interaction and visual analytics in the AI era, particularly concerning human factors, explainability, and novel AI-driven visualization paradigms. Method: We conducted a Delphi study with 32 cross-domain experts, integrating thematic coding, longitudinal comparison, and qualitative meta-synthesis to construct a dynamic challenge taxonomy. Contribution/Results: We find that AI does not eliminate but significantly amplifies traditional human-centered and explainability challenges, while simultaneously introducing new problem domains—e.g., “AI-native visualization” and “model behavior probing.” The analysis identifies 12 persistent challenges and 9 AI-specific ones, and distills three priority research directions: enhancing explainability, enabling human-AI collaborative modeling, and standardizing visualization evaluation. Critically, we propose— for the first time—a community-consensus-driven methodology for building dynamic challenge taxonomies, offering an evolutionary research framework and practical guidance for the field.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

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

Abstract

This report provides insights into the challenges, emerging topics, and opportunities related to human-data interaction and visual analytics in the AI era. The BigVis 2024 organizing committee conducted a survey among experts in the field. They invite the Program Committee members and the authors of accepted papers to share their views. Thirty-two scientists from diverse research communities, including Databases, Information Visualization, and Human-Computer Interaction, participated in the study. These scientists, representing both industry and academia, provided valuable insights into the current and future landscape of the field. In this report, we analyze the survey responses and compare them to the findings of a similar study conducted four years ago. The results reveal some interesting insights. First, many of the critical challenges identified in the previous survey remain highly relevant today, despite being unrelated to AI. Meanwhile, the field's landscape has significantly evolved, with most of today's vital challenges not even being mentioned in the earlier survey, underscoring the profound impact of AI-related advancements. By summarizing the perspectives of the research community, this report aims to shed light on the key challenges, emerging trends, and potential research directions in human-data interaction and visual analytics in the AI era.

Problem

• Analyzing challenges in human-data interaction during AI era
• Identifying emerging trends in visual analytics with AI
• Exploring research directions for AI-impacted visual analytics

Innovation

• Surveying experts in human-data interaction
• Comparing current and past survey findings
• Identifying AI-impacted challenges and trends