# Designing escalation criteria for international AI incident response: criteria, triggers, and thresholds

arXiv: https://arxiv.org/abs/2604.23183 · HTML: https://mlsanity.com/paper/2604.23183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 25 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [Open Problems in Technical AI Governance](https://mlsanity.com/paper/2407.14981/) (2024)
- [Who's in Charge? Disempowerment Patterns in Real-World LLM Usage](https://mlsanity.com/paper/2601.19062/) (2026)
- [A taxonomic system for failure cause analysis of open source AI incidents](https://mlsanity.com/paper/2211.07280/) (2022)
- [In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI](https://mlsanity.com/paper/2503.16861/) (2025)
- [Lessons for Editors of AI Incidents from the AI Incident Database](https://mlsanity.com/paper/2409.16425/) (2024)
- [From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms](https://mlsanity.com/paper/2502.08166/) (2025)
- [Technical Requirements for Halting Dangerous AI Activities](https://mlsanity.com/paper/2507.09801/) (2025)

## Abstract (arXiv)

AI incident reporting requirements are emerging in regulation and policy, yet no operational criteria exist for determining when a detected AI incident warrants escalation beyond national handling to international coordination. This paper proposes an escalation framework to address this gap, intended as a common reference point across jurisdictions that enables aligned escalation while preserving flexibility in how actors respond within their own legal and policy contexts. We review SB 53, the EU AI Act, the GPAI Code of Practice, and incident frameworks from other industries to derive eight criteria for assessing whether an incident warrants escalation, translated into a sequential flowchart with gated decision points and threshold checks. For each criterion, we map how it interplays with these regulatory frameworks, identifying where their design choices support or undermine effective detection. We test the framework against ten documented AI incidents and structured variants to identify where criteria under-detect or misclassify incidents in practice. We find three design patterns that may lead to systematic under-detection in regimes where model developers are responsible for escalation: a. where escalation requires confirmed harm, events such as model weight exfiltration risk detection only after severe, irreversible harm has propagated; b. where incidents are assessed individually, systemic harms emerging from accumulation risk being under-detected; and c. where thresholds align with legal instruments rather than quantitatively testable terms, criteria risk being impractical to apply under time pressure. We also find that escalation rules are only one component of a broader framework: the underlying definitions against which thresholds are set, and the data available to the responsible actor, create interdependencies that can themselves drive under-detection.