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Sprint projectJan 11, 2026Durham

Evaluating the Technical Effectiveness and Legislative Practicality of AI Safety Frameworks

Desmond Gatling

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

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Report: Evaluating the Technical Effectiveness and Legislative Practicality of AI Safety Frameworks

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I developed a Governance Proposal to bridge the gap between AI safety benchmarks and real-world legislation. By evaluating six regulatory frameworks against the FAITH-CoT benchmark, I identified a "Verification Bottleneck" where the most effective safety tools currently face the highest legislative friction. My research provides a diagnostic tool for policymakers to transition from "trust-based" self-reporting to active, benchmark-driven verification. This framework allows for "Regulatory Triage," identifying the specific policy characteristics needed to make frontier AI development measurably safer and more transparent.

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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 paper investigates the "Verification Bottleneck" in AI governance by proposing a "Socio-Technical Matrix" to measure the trade-offs between technical safety (via the FAITH-CoT benchmark) and legislative feasibility. However, it is unclear what new information is derived from this work, as the core insight that regulations requiring granular model access are politically harder to pass is already a well-understood dynamic.

    To provide real utility, the author needs to justify the construction of the matrix, as the current 0-2 scoring system appears subjective and "made up" rather than grounded in established standards. Similarly, the 85% faithfulness threshold is presented as arbitrary; the author claims it mirrors aviation or medical standards but fails to provide any citations to support this. Finally, the submission needs significant polish: the text is full of OCR-like typos (e.g., "ansIrs"), crucial figures (Figure 1 and 2) are missing, and random parts of the text are highlighted without a clear purpose.

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  2. Unfortunately, even if the author's work has impact or novelty, it is very hard to decipher it. The submission's document is hard to follow, and it unfortunately has a lot of issues with formatting. To make matters worse, the graphs are missing from the paper, which doesn't allow to understand submission's evaluations' results.

    I enjoy the idea of a theoretical framework for evaluating AI Safety policies. I think right now AI policy work is highly disorganised and unclear, especially for technical professionals, and there is great value in improving our understand of what policies are efficient and practical.

    This submission does try to address that, which is why I give it higher score on impact potential: I encourage the author to lay out their thoughts in a clearer manner (frontier LLMs might be able to assist with that) and review issues with missing graphs, and continue working in this direction.

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Cite this project

@misc{gatling2026evaluating,
  title = {{Evaluating the Technical Effectiveness and Legislative Practicality of AI Safety Frameworks}},
  author = {Desmond Gatling},
  year = {2026},
  month = jan,
  note = {Submitted to AI Manipulation Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/evaluating-the-technical-effectiveness-and-legislative-practicality-of-ai-safety-frameworks-t2ku}},
  url = {https://apartresearch.com/sprints/projects/evaluating-the-technical-effectiveness-and-legislative-practicality-of-ai-safety-frameworks-t2ku}
}

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