AI Incident Monitoring through a Public Health Lens

2026-04-21

AI Summary

This study addresses the challenge of quantifying AI-related risks—defined as the joint measure of probability and severity—due to the absence of systematic data on AI adoption rates and incident reporting in existing AI accident databases, which hinders balanced assessments of AI’s benefits versus its risks. To overcome this limitation, the work innovatively adapts disease surveillance paradigms from public health and proposes a six-stage framework for characterizing the evolution of AI incidents. By integrating expert judgment, statistical modeling, and visualization tools, the framework enables the identification of an incident’s current stage within this progression. Empirical validation through case studies on autonomous driving (using real-world mileage-based accident rates) and deepfakes demonstrates both the feasibility of the approach and its potential to offer actionable pathways for AI risk management and informed public policy decisions.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: AI for Accessibility
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Artificial intelligence systems are now deployed at scale across sectors, accompanied by a growing number of real-world incidents ranging from misinformation and cybercrime to autonomous-system failures. Databases of AI incidents index these events, but they cannot measure ``risk'' (i.e., a joint measure of likelihood and severity) without additional data regarding the prevalence of risk-associated systems and their incident reporting rates. As a result, policymakers, companies, and the general public lack a means to weigh the benefits of AI against their in-context risks. Inspired by public-health processes, which presume noisy and incomplete disease surveillance, we identify six phases of incident emergence. We demonstrate the framework through a detailed case study of autonomous vehicles, whose mandatory reporting requirements produces reliable incident-rate ground truth expressed in distance traveled. The case study shows that an informed panel of domain experts (e.g., self-driving experts) can combine their domain expertise, incident data, and a collection of statistical and visualization tools to arrive at incident phase determinations serving public needs. We further demonstrate the approach with a deepfake incident case study and chart a path for future research in incident phase determination.

Problem

• AI risk
• incident monitoring
• risk assessment
• public health
• autonomous systems

Innovation

• AI incident monitoring
• risk assessment
• public health framework
• incident phase determination
• expert-informed analysis