Healthy skepticism in AI: a data visualization research agenda

2026-10-07

AI Summary

This study addresses the critical issue that users’ over-reliance on artificial intelligence undermines their critical thinking, necessitating a balance between trust and healthy skepticism. By integrating theories from data visualization and human-computer interaction, this work explores intervention mechanisms designed to mitigate AI over-reliance and proposes a novel research agenda that reconciles user trust with appropriate skepticism. The primary contributions include systematically delineating the risk boundaries of AI over-reliance and identifying visualization-based intervention strategies. Ultimately, this research establishes both a theoretical framework and practical pathways for fostering rational decision-making within human-AI collaboration.

Abstract

Research in data visualization of artificial intelligence (AI) models has historically focused on enhancing trust through visual explanations of AI. The trustworthiness line of work was built at least partially on an assumption that humans were critical users unlikely to adopt AI technology. It is increasingly clear that human trust levels in AI span, in fact, a wide range from critical to over-reliant. There is an urgent need to support both trust and healthy skepticism in AI solutions. We argue that it is healthy for humans to adopt a skeptical view both on the results of AI models and on the use of such AI models. We share our thoughts on the rising phenomenon of over-reliance on AI models, the risks and opportunities in using AI models, and the role of data visualization in over-reliance situations where humans are not motivated to engage in critical thinking.

Problem

• Artificial Intelligence
• Data Visualization
• Over-reliance
• Healthy Skepticism
• Trust

Innovation

• Data Visualization
• Healthy Skepticism
• Over-reliance
• Trust in AI
• Critical Thinking