AI Incident Monitoring through a Public Health Lens
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910156596445184 |
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| author | Abraham, Sophia Chen, Taiye Chhun, Cyril Jaramillo-Gutierrez, Giovanna Mylius, Simon Raaj, Sayash Slattery, Peter McGregor, Sean |
| author_facet | Abraham, Sophia Chen, Taiye Chhun, Cyril Jaramillo-Gutierrez, Giovanna Mylius, Simon Raaj, Sayash Slattery, Peter McGregor, Sean |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19914 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI Incident Monitoring through a Public Health Lens Abraham, Sophia Chen, Taiye Chhun, Cyril Jaramillo-Gutierrez, Giovanna Mylius, Simon Raaj, Sayash Slattery, Peter McGregor, Sean Computers and Society 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. |
| title | AI Incident Monitoring through a Public Health Lens |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2604.19914 |