Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities

• URL: http://arxiv.org/abs/2603.05542v1
• Date: Wed, 04 Mar 2026 14:18:17 GMT
• Title: Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities
• Abstract summary: The rapid advancement of AI is transforming human-centered systems.<n>This paper investigates the challenges introduced by recent advances in AI and examines how these developments are reshaping the ways users engage with data.<n>It outlines limitations and open research directions for building human-centered AI systems for interactive data analysis in the AI era.
• Score: 34.82163910275556
• License: http://creativecommons.org/licenses/by/4.0/
• 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, and incorporating cognitive, perceptual, and design principles into every level of the human-data interaction stack. This paper investigates the challenges introduced by recent advances in AI and examines how these developments are reshaping the ways users engage with data, while outlining limitations and open research directions for building human-centered AI systems for interactive data analysis in the AI era.

Related papers

• Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies [25.913918889459552]
  AI design characteristics and human personality traits each impact the quality and outcomes of human-AI interactions.<n>This study compares a purely simulated dataset comprising 2,000 simulations and a parallel human subjects experiment involving 290 human participants.<n>Results reveal divergences between purely simulated and human study datasets, and between scenario types.
  arXiv  Detail & Related papers  (2026-04-17T01:10:34Z)
• Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations [63.80827184637476]
  We introduce D-STAR, a hierarchical policy that disentangles when to act from where to act.<n>We validate our framework through extensive and rigorous simulations.
  arXiv  Detail & Related papers  (2026-01-14T14:37:06Z)
• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
  We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
  arXiv  Detail & Related papers  (2025-06-05T20:48:16Z)
• Artificial Behavior Intelligence: Technology, Challenges, and Future Directions [1.5237607855633524]
  This paper defines the technical framework of Artificial Behavior Intelligence (ABI)<n>ABI comprehensively analyzes and interprets human posture, facial expressions, emotions, behavioral sequences, and contextual cues.<n>It details the essential components of ABI, including pose estimation, face and emotion recognition, sequential behavior analysis, and context-aware modeling.
  arXiv  Detail & Related papers  (2025-05-06T08:45:44Z)
• Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions [0.8249694498830561]
  Alzheimer's Disease (AD) is marked by significant inter-individual variability in its progression.<n>This review aims to consolidate current knowledge and guide future efforts in developing clinically relevant AI tools for personalized AD prognostication.
  arXiv  Detail & Related papers  (2025-04-29T21:45:28Z)
• From Consumption to Collaboration: Measuring Interaction Patterns to Augment Human Cognition in Open-Ended Tasks [2.048226951354646]
  The rise of Generative AI, and Large Language Models (LLMs) in particular, is fundamentally changing cognitive processes in knowledge work.<n>We present a framework that analyzes interaction patterns along two dimensions: cognitive activity mode (exploration vs. exploitation) and cognitive engagement mode (constructive vs. detrimental)
  arXiv  Detail & Related papers  (2025-04-03T17:20:36Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Towards interactive evaluations for interaction harms in human-AI systems [8.989911701384788]
  We propose a shift towards evaluation based on textitinteractional ethics, which focuses on textitinteraction harms<n>First, we discuss the limitations of current evaluation methods, which (1) are static, (2) assume a universal user experience, and (3) have limited construct validity.<n>We present practical principles for designing interactive evaluations. These include ecologically valid interaction scenarios, human impact metrics, and diverse human participation approaches.
  arXiv  Detail & Related papers  (2024-05-17T08:49:34Z)
• Can AI Serve as a Substitute for Human Subjects in Software Engineering Research? [24.39463126056733]
  This vision paper proposes a novel approach to qualitative data collection in software engineering research by harnessing the capabilities of artificial intelligence (AI) We explore the potential of AI-generated synthetic text as an alternative source of qualitative data. We discuss the prospective development of new foundation models aimed at emulating human behavior in observational studies and user evaluations.
  arXiv  Detail & Related papers  (2023-11-18T14:05:52Z)
• Human-AI Coevolution [48.74579595505374]
  Coevolution AI is a process in which humans and AI algorithms continuously influence each other. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
  arXiv  Detail & Related papers  (2023-06-23T18:10:54Z)
• Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research [69.48907856390834]
  Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot. This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
  arXiv  Detail & Related papers  (2021-10-14T15:14:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.