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Sprint projectSep 11, 2026Chicago, Illinois, USA

From Warning Shot to Supervisory File

Jennifer Dickey

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

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Report: From Warning Shot to Supervisory File

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This project develops a regulator ready response framework for the OpenAI Hugging Face incident under the EU AI Act. Using the public incident record, EU AI Act Articles 55, 91, 92, 93, and 101, and the European Commission’s serious incident reporting framework, the project identifies which facts remain unresolved and what evidence a regulator would need to assess compliance and systemic risk. Its primary output is a proposed 24 item Article 91 Request for Information that specifies the evidence sought, what would constitute a sufficient response, what would remain inadequate, and the potential supervisory consequence of unresolved gaps. A parallel stress test of the Commission’s serious incident reporting template found that 8 of 9 reporting fields retain a material information gap when completed from the public record alone, highlighting weaknesses in how current reporting tools capture autonomous AI incidents that begin in controlled testing but extend into third party systems.

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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. "Only one of nine fields is substantially answerable from public sources; eight retain gaps." -- The AIO is learning, and feedback (with evidence) like this is the kind of practical information that makes regulators better. I'd love to see this as a policy memo to the AIO with recommendations on transparency and public disclosure requirements.

  2. A strong project and request that asks most of the right questions. Particularly good is the mapping of what a sufficient and an insufficient response would look like for each item.

    It would benefit from in-depth legal review, starting with proportionality. Article 91(1) reaches information "necessary" to assess compliance, and each item carries a legal-relevance line, but necessity is asserted rather than argued. Are all items strictly necessary, and what is the test for this under EU law and the AI Act?

    A related question the paper touches upon but does not pursue: OpenAI is a signatory to the Code of Practice, so how much of what is requested here is already held by the AI Office through the Safety and Security Framework and the model reports? Establishing that overlap would sharpen the request and strengthen the necessity case for what remains.

    Appendix D reads as an actionable proposal. One caveat: framing the supplemental fields for incidents arising "during model evaluation" reopens the applicability question the paper handles well elsewhere.

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  3. Very well sourced and referenced. Strong methodology that clearly outlines a procedure and structure for turning "evidence to action". Addresses a meaningful gap and contributes a useful, actionable instrument and new reporting fields that regulators could use.

    The outlined sufficient/insufficient criteria could be tested against and applied to existing disclosures.

Cite this project

@misc{dickey2026from,
  title = {{From Warning Shot to Supervisory File}},
  author = {Jennifer Dickey},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/from-warning-shot-to-supervisory-file-3s5y}},
  url = {https://apartresearch.com/sprints/projects/from-warning-shot-to-supervisory-file-3s5y}
}

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