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Sprint projectSep 14, 2026istanbul

Strengthening International AI Incident Discovery

İpek Cerrahoğlu, Ömer Bilgin, Aydin Yigit Alfatli · Team A-O-I

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Strengthening International AI Incident Discovery

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Mandatory AI incident reporting exists to make dangerous events visible to regulators while there is still time to act. We coded the six such regimes in force or set to be enforced soon from primary legal texts and applied each trigger to the documented loss of control incidents of summer 2026, finding that none clearly compel a report. Disaster-scale thresholds are only part of the defect: most triggers require harm before engaging, almost every text exempts evaluation findings, and no binding instrument requires near misses to be reported. We set out recommendations for four stakeholder groups, mostly needing no new legislation: a reporting ladder whose incident tiers capture all real-world harm below the crisis threshold, measurable near-miss triggers with denominators developers can adopt unilaterally, and information-sharing channels between jurisdictions.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. This is interesting analysis that supports the claim that most people agree with -- we're not doing a good job defining serious incidents and when they should be reported. I particularly appreciate recommendation 6.3.1 (Fund the external detection layer as reporting infrastructure) and would recommend follow up with grant organizations or "applied" fellowships to explore what this could look like in the real world.

Cite this project

@misc{cerrahoglu2026strengthening,
  title = {{Strengthening International AI Incident Discovery}},
  author = {İpek Cerrahoğlu and Ömer Bilgin and Aydin Yigit Alfatli},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/strengthening-international-ai-incident-discovery-d0t9}},
  url = {https://apartresearch.com/sprints/projects/strengthening-international-ai-incident-discovery-d0t9}
}

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