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Sprint projectSep 14, 2026Melbourne & London

Evidence and Access in the OpenAI-Hugging Face Incident

Martin Radzaj, Joseph O'Neill, Sofiia Lobanova · Team MJS

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

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Report: Evidence and Access in the OpenAI-Hugging Face Incident

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This paper examines the current problem of obtaining evidence (comparing California’s SB 53 with the EU AI Act) required for efficient incident investigation. We do so via the OpenAI-Hugging Face incident and recommend connecting reporting duties with requirements for reliable records, mandatory evidence preservation triggered after incidents, and independent auditor access.

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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. I am admittedly not a lawyer, but this was the first time I'd read such thorough research on the idea of protecting evidence and the existing legal gaps that don't compel labs to keep these logs. I'd be interested in reading more analysis about whether existing legal frameworks are sufficient for regulators to compel labs to comply with these stringent record requirements, or will new laws/amendments be required?

  2. The central insight, which is that incident reporting duties without record-keeping, preservation of evidence, and independent evaluator access leave investigations dependent on developer cooperation, is valuable and policy-relevant. The legal analysis would be stronger if California's SB 53 and the EU AI Act were compared side by side, and if these two pieces of legislation were tested against the incidents.

Cite this project

@misc{radzaj2026evidence,
  title = {{Evidence and Access in the OpenAI-Hugging Face Incident}},
  author = {Martin Radzaj and Joseph O'Neill and Sofiia Lobanova},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/evidence-and-access-in-the-openaihugging-face-incident-wtyv}},
  url = {https://apartresearch.com/sprints/projects/evidence-and-access-in-the-openaihugging-face-incident-wtyv}
}

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