Visualizing Uncertainty-to-Action Composition for Human Oversight
2026-08-17 • Human-Computer Interaction
Human-Computer InteractionArtificial Intelligence
AI summaryⓘ
The authors explain that while AI often shows how unsure it is about its decisions, it usually does not say what should be done about that uncertainty. They create a new way to combine different uncertainty signals into a clear decision about whether and how humans should oversee the AI's suggestions. To help people understand this process, they develop a visualization tool called ActionCue that makes the AI's uncertainty and oversight signals easy to see. They test this approach using examples from healthcare, credit checks, and disaster prediction. Their work helps make AI uncertainty responses clearer and easier to interpret.
Artificial IntelligenceUncertainty VisualizationDecision MakingOversightHuman-AI InteractionProcess TransparencyPrecedence PolicyContextual SafetyConfidenceActionCue
Authors
Chisom Anyabolu, Akshat Dubey, Georges Hattab
Abstract
Artificial intelligence systems often disclose uncertainty, yet they rarely make clear what response that uncertainty should trigger. Most uncertainty visualizations encode uncertainty in model outputs, leaving users to discern the most appropriate course of action. A second region of the design space--uncertainty in the decision process itself, including how multiple uncertainty conditions compose into an oversight response-- remains comparatively underexplored. We address this gap with two coupled contributions. First, we introduce an uncertainty-to-action binding framework that composes multiple uncertainty conditions into a single oversight response under a precedence policy with a contextual safety modifier. That response concerns whether and how an AI-supported decision may proceed, not the substantive domain decision itself. Second, we present ActionCue, a process-transparency visualization that renders that composition explicit. We demonstrate the approach through a three-way comparison with confidence-only and data-level uncertainty displays, using worked cases from healthcare, credit assessment, and disaster forecasting. Together, the framework specifies how uncertainty conditions are resolved into an oversight response, and the visualization makes that resolution inspectable rather than implicit.