Lessons for Editors of AI Incidents from the AI Incident Database
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As artificial intelligence (AI) systems become increasingly deployed across the world, they are also increasingly implicated in AI incidents – harm events to individuals and society. As a result, industry, civil society, and governments worldwide are developing best practices and regulations for monitoring and analyzing AI incidents. The AI Incident Database (AIID) is a project that catalogs AI incidents and supports further research by providing a platform to classify incidents for different operational and research-oriented goals. This study reviews the AIID’s dataset of 750+ AI incidents and two independent taxonomies applied to these incidents to identify common challenges to indexing and analyzing AI incidents. We find that certain patterns of AI incidents present structural ambiguities that challenge incident databasing and explore how epistemic uncertainty in AI incident reporting is unavoidable. We therefore report mitigations to make incident processes more robust to uncertainty related to cause, extent of harm, severity, or technical details of implicated systems. With these findings, we discuss how to develop future AI incident reporting practices.
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Taxonomies based on incident databases and media reports capture risks that have already occurred and were visible enough to be recorded, and are therefore less suited to identifying risks that develop gradually or have not yet produced visible harms.
“Third, taxonomies based on incident databases and media reports (Abercrombie et al. 2024;Turri and Dzombak 2023;Paeth et al. 2025) capture risks that have already occurred and were visible enough to be recorded.They are therefore less suited to identifying risks that develop gradually or have not yet produced visible harms, such as the erosion of professional skills caused by prolonged reliance on”
From Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents (2026) · cites this paper as Paeth et al. 2025
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Cited by
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Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents
La Malfa, G., Meegahapola, L., Bogucka, E., Zhang, J. M., Luck, M., Black, E., & Quercia, D. (2026) · arXiv · doi:10.48550/arxiv.2608.08601
- Authors
- Paeth, K., Atherton, D., Pittaras, N., Frase, H., & McGregor, S.
- Affiliations
- UL Research Institutes (US)
- Georgetown University (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2025-04-11
- Volume
- 39
- Issue
- 28
- Pages
- 28946-28953
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4409381804
- Cited by
- 23
- References
- 0
- Citations by year
- 2026: 14 · 2025: 8 · 2024: 1
- FWCI
- 16.18
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-sa)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- AI incidents, AI Incident Database, incident reporting, epistemic uncertainty, AI regulation, AI monitoring, reporting practices
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