Open Problems in AI Incident Governance

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Harleen Kaur Sidhu, Rebecca Scholefield, Nour Annan, Kevin Hernandez, Isabel Nieh Hou, Abdulrahman Alshaikhi, Ze Shen Chin, Rokas Gipiškis
• Venue: arXiv: cs.CY
• Published: 2026-07-06
• Collected: 2026-07-07
• URL: http://arxiv.org/abs/2607.05163v1
• PDF: https://arxiv.org/pdf/2607.05163v1
• DOI: UNKNOWN

Plain-Language Brief

The paper discusses issues with current AI incident governance systems, specifically highlighting inconsistencies in definitions, classification, monitoring, and reporting across various frameworks.

What the Document Says

• AI incidents may occur post-deployment that were not anticipated during safety assessments.
• Adequate AI incident governance is crucial, involving clear definitions, taxonomies, monitoring practices, reporting mechanisms, and analysis.
• Existing frameworks for AI incident governance by regulatory bodies and independent efforts have inconsistencies in aspects such as definitions, classification, monitoring, and reporting.

Key Concepts Mentioned

• AI incidents
• AI incident governance
• definitions
• taxonomies
• monitoring practices
• reporting mechanisms
• incident analysis

Claims Made by the Document

• Existing AI incident governance frameworks have inconsistencies.

Evidence Used

• Inconsistencies exist in the types, categorization, and reporting of incident data across various frameworks.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

AI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate. Managing these failures requires what we refer to as adequate \textit{AI incident governance}, where having good definitions, taxonomies, monitoring practices, reporting mechanisms, and incident analysis is essential. We examine existing frameworks related to AI incident governance by regulatory bodies and independent efforts, and find that while there are frameworks that describe how individual functions can be performed, there is a lack of consistency within the aspects of definitions, classification, monitoring, and reporting. These inconsistencies apply to the types of incident data that is collected and reported, the ways in which they are categorised, and as a result, the depth, representativeness, and accuracy of analysis that can be performed.