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Sprint projectSep 14, 2026Melbourne, Australia

Beyond Disclosure: An Evidence-Based Framework for AI Incident Communication

Luka Janicijevic, Leyla Amur, Aditya Singh · Team Disclosure

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

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Report: Beyond Disclosure: An Evidence-Based Framework for AI Incident Communication

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We developed a rubric for assessing public communication by frontier AI labs, model developers and AI infrastructure providers. Our research base includes more than 300 academic, regulatory and professional sources and public records. The rubric contains 15 criteria across seven communication areas and requires dated evidence for each assessment.

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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 would have liked to have seen more discussion in the main report about the 15 questions, as well as more details about how the incident scored against your rubric. Overall, this is a relevant problem to explore, especially as labs continue to grapple with disclosure (and, sadly, blame a lack of standard as the reason they don't disclose).

  2. It is generally a valuable and timely endeavour trying to create structures that compel AI companies to communicate better and give outside observes a consistent way to hold labs accountable, as well as raise the standard of incident reporting. The framework's main conceptual gap is that it largely assumes more public disclosure is better. It does not critically examine which information should be made public and which information should be contained to securitised channels between AI companies and regulators or relevant agencies.

Cite this project

@misc{janicijevic2026beyond,
  title = {{Beyond Disclosure: An Evidence-Based Framework for AI Incident Communication}},
  author = {Luka Janicijevic and Leyla Amur and Aditya Singh},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/beyond-disclosure-an-evidencebased-framework-for-ai-incident-communication-ug0i}},
  url = {https://apartresearch.com/sprints/projects/beyond-disclosure-an-evidencebased-framework-for-ai-incident-communication-ug0i}
}

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