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Sprint projectSep 13, 2026Ankara

AI Incident Timeline & Evidence Dashboard

Hüseyin Tenlik, Emre Bilgiç, Deniz Süren · Team 10

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

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Report: AI Incident Timeline & Evidence Dashboard

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The 2026 OpenAI–Hugging Face incident produced overlapping but non-identical public accounts from the organizations involved, independent investigators, and secondary analysts. We ask whether a claim-level evidence model can make agreement, disagreement, and uncertainty easier to verify. We reviewed six incident-specific public sources and encoded 30 dated events and interpretive claims, each linked to a primary source and labeled Confirmed, Disputed, or Uncertain under an explicit rubric. We then built a React/Express dashboard that supports filtering, record-level evidence inspection, source links, and eight evidence-derived “Checks / Watch Next” questions. The final dataset contains 21 Confirmed, five Disputed, and four Uncertain records. The main result is not a new forensic finding, but a transparent evidence layer that preserves source conflicts and open questions rather than collapsing the incident into one narrative.

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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 project is well executed but I struggle to see how useful it is in the grander scheme of AI security. I could see future versions of this being implemented in existing AI security databases as a way to track ongoing incidents.

  2. The strongest part of this project is the decision to treat the incident as a set of evidence rather than trying to force everything into one clean story. That works especially well where the sources disagree. The stopping-date issue is a good example: one source effectively ends the campaign on 12 July, while Hugging Face still records 1,130 actions on 13 July. The restart timeline has a similar problem, and both would be easy to miss in a normal narrative. The JSON dataset, schema, API filtering, citation files, MIT licence and CI workflow make this something other researchers could genuinely reuse, not just a dashboard. The main weakness is source coverage, especially the missing 37-page technical report released with the August post, which may affect the 27 June alert classification. I would also separate first-party confirmation from independent corroboration, since 11 of the 21 Confirmed records rely only on first-party reporting. Disputed should also distinguish actual source conflicts from interpretations being tested. Finally, the checks would be much stronger if each had a clear pass, fail and resolution condition.

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Cite this project

@misc{tenlik2026ai,
  title = {{AI Incident Timeline \& Evidence Dashboard}},
  author = {Hüseyin Tenlik and Emre Bilgiç and Deniz Süren},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/ai-incident-timeline-evidence-dashboard-vfjq}},
  url = {https://apartresearch.com/sprints/projects/ai-incident-timeline-evidence-dashboard-vfjq}
}

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