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Sprint projectSep 14, 2026Berkeley, CA, USA

AI Warning Shots: Improved Definitions & Analytic Frameworks for Effective Governance Response to AI Incidents

Erik Leklem · Team AI Warning Shots Team

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

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Report: AI Warning Shots: Improved Definitions & Analytic Frameworks for Effective Governance Response to AI Incidents

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AI incident response would benefit from more robust scientific and public policy formulation frameworks for capitalizing on warning shots in order to improve AI governance. We show how the OpenAI/Hugging Face incident is an AI warning shot, and why. We present a clear definition of what an AI warning shot is, with five associated scoring criteria, in order to address definitional gaps in the field of AI safety. Additionally, we apply our proposed Governance Conversion Framework (GCF) to the incident to demonstrate how the U.S. government and the European Union are responding, and at what stage governance conversion is occurring (or not). We postulate that the European Union is more likely than the United States to respond effectively in the near-term. We share our ongoing research work (building upon a SPAR Research Program project), warning shot definition, criteria, and associated governance framework with the broader AI safety and governance community. We do so in the hope of facilitating improved analysis and comparative research that can accelerate effective responses to AI incidents of today and the future.

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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?

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  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.

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  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.
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  1. This research highlights a potential blind-spot in the policy landscape - how can society capitalize on events, termed "warning shots", with limited harm or fallout effectively? This is especially important for transformative AI technology where we don't the preconditions or the capability level needed to cause a catastrophic event.

    It applies a framework for analyzing warning shots across multiple criterions to the recent OpenAI/HF incident in a convincing way. They also identify the pathway to action that these events take through existing governance structures and compare the readiness for governance action between the EU and US. The identified gap in US governance structures is the most convincing part of this work and is a cause for concern given their position.

Cite this project

@misc{leklem2026ai,
  title = {{AI Warning Shots: Improved Definitions \& Analytic Frameworks for Effective Governance Response to AI Incidents}},
  author = {Erik Leklem},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/ai-warning-shots-improved-definitions-analytic-frameworks-for-effective-governance-response-to-ai-incidents-u363}},
  url = {https://apartresearch.com/sprints/projects/ai-warning-shots-improved-definitions-analytic-frameworks-for-effective-governance-response-to-ai-incidents-u363}
}

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