Skip to content
Sprint projectSep 14, 2026London

ScopeAI

Tammy

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

This project investigates: How completely do the public investigations cover a predefined set of incident-accountability questions, who set their boundaries, and which answers remain dependent on the operator's own account?

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

Only the Apart team reads this, and funders if you agree below.

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

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 research is useful to highlight the gaps exposed by the lack of legal requirements for comprehensive third party access to labs' internal work. More policymakers need to be aware of these scope limitations as it pertains to "after action reports." Would love to see this frameworks opened up and generalized a bit such that it is applicable to future incidents without having to re-write the questions. I really like the idea of a Disclosure Minimum.

  2. This work makes a useful distinction between independent analysis and independently determined investigation scope, and turns it into a practical disclosure checklist.

    Its treatment of the external investigators’ contributions and limitations is balanced. Clearer criteria for what counts as full coverage, including the distinction between remediation plans and demonstrated effectiveness, would make the coding easier to interpret and reproduce. An independent coding pass and a small sensitivity analysis would strengthen confidence in the headline percentages. Larger text and more selective use of tables would also improve readability.

  3. I think ScopeAI makes a useful distinction because an independent investigation can still leave important questions outside its assigned scope. The paper maps which questions an external AI incident assessment addresses and proposes requests for further information. I appreciated that it recognizes the assessment’s value rather than treating unanswered questions as evidence that the investigators lacked independence.

    The numerical findings need correction before others rely on them. The scores shown in the paper do not match one of its reported percentages, which makes the size of the claimed gap uncertain. I would reconcile those figures and ask another reviewer to apply the scoring rules to the same sources. The broader concern is worth examining, but missing coverage alone does not establish misconduct or unreliable findings.

Cite this project

@misc{tammy2026scopeai,
  title = {{ScopeAI}},
  author = {Tammy},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/scopeai-v1hd}},
  url = {https://apartresearch.com/sprints/projects/scopeai-v1hd}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026