Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities
Jean-Daniel Fekete, Yifan Hu, Dominik Moritz, Arnab Nandi, Senjuti Basu Roy, Eugene Wu, Nikos Bikakis, George Papastefanatos, Panos K. Chrysanthis, Guoliang Li, Lingyun Yu

TL;DR
This paper explores the evolving landscape of human-data interaction and visualization in the AI era, highlighting challenges like uncertainty and scalability, and proposing new design principles for human-centered AI systems.
Contribution
It analyzes recent AI advancements' impact on human-data interaction, identifying challenges and proposing a framework for designing more effective human-centered AI systems.
Findings
AI introduces new uncertainty and scalability challenges in data analysis.
Existing interaction paradigms are insufficient for large-scale, multimodal data.
Redefining human-machine roles is essential for effective AI-driven data analysis.
Abstract
The rapid advancement of AI is transforming human-centered systems, with profound implications for human-AI interaction, human-data interaction, and visual analytics. In the AI era, data analysis increasingly involves large-scale, heterogeneous, and multimodal data that is predominantly unstructured, as well as foundation models such as LLMs and VLMs, which introduce additional uncertainty into analytical processes. These shifts expose persistent challenges for human-data interactive systems, including perceptually misaligned latency, scalability constraints, limitations of existing interaction and exploration paradigms, and growing uncertainty regarding the reliability and interpretability of AI-generated insights. Responding to these challenges requires moving beyond conventional efficiency and scalability metrics, redefining the roles of humans and machines in analytical workflows,…
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Taxonomy
TopicsData Visualization and Analytics · Explainable Artificial Intelligence (XAI) · Personal Information Management and User Behavior
