Data visualization promotes healthy skepticism towards AI models
Healthy skepticism in AI: a data visualization research agenda
Human-Computer InteractionArtificial Intelligence
Summary
People tend to either trust or rely too much on AI systems without questioning them. The authors point out that it's important for humans to stay skeptical about AI's results and usage. They discuss how visualization can help people see risks and avoid blindly trusting AI outputs. Their paper suggests a direction for creating AI tools that encourage critical thinking without overwhelming users.
What this means in practice
- •For user interface designers: Design visual tools that encourage users to question AI outputs and reduce blind trust in automated decisions.
- •For risk management teams: Incorporate visualization techniques that highlight uncertainties and limitations in AI analyses to support cautious decision-making.
A position paper. It proposes an approach and reports no results.
Authors
G. Elisabeta Marai, Marc Baaden, Michael Behrisch, Michael Krone, Pere-Pau Vázquez
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.