AI Incident Monitoring through a Public Health Lens

Sophia Abraham; Taiye Chen; Cyril Chhun; Giovanna Jaramillo-Gutierrez; Simon Mylius; Sayash Raaj; Peter Slattery; Sean McGregor

arXiv · 2604.19914

AI Incident Monitoring through a Public Health Lens

Sophia Abraham·Taiye Chen·Cyril Chhun·Giovanna Jaramillo-Gutierrez·Simon Mylius·Sayash Raaj·Peter Slattery·Sean McGregor

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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.

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Sophia Abraham, Taiye Chen, Cyril Chhun, Giovanna Jaramillo-Gutierrez, Simon Mylius, Sayash Raaj, Peter Slattery, Sean McGregor. 2026-04-21. AI Incident Monitoring through a Public Health Lens. https://arxiv.org/abs/2604.19914

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