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Sprint projectJun 22, 2026Merida

Protección de la soberanía: un marco empírico de vulnerabilidad y un escudo regulatorio para la IA en una potencia media

Max Pinelo, Pilar Moncada · Team TAISO

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

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Report: Protección de la soberanía: un marco empírico de vulnerabilidad y un escudo regulatorio para la IA en una potencia media

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A reproducible framework to defend the AI sovereignty of a tech-dependent middle power.

VIGÍA (live) reads the official gazette and the national press across 15 risk categories to measure where the state runs high-risk AI unregulated and on whom it depends. SARA turns that diagnosis into five prioritized laws that make that dependence non-weaponizable.

Evidence (Mexico): Analysis of 153 gazette editions and 4,327 news items reveals:

95.6% regulatory gaps, 82% "silent AI", and 72.8% dependence on six U.S. firms.

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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. The paper's greatest strength (a real, operational system producing real data) is also where the greatest methodological gap lies. The reliability of the entire downstream analysis depends on the accuracy of the LLM classification pipeline, and the paper does not give the reader enough to evaluate this.

    The SARA framework is the paper's most ambitious contribution and also the least developed. The pillars are well-motivated, but each one encounters known political economy obstacles that the paper doesn't address. For example, anti-monopoly legislation (Pillar 4) faces trade agreement constraints the paper briefly acknowledges but doesn't engage with; the case for "legislate before you negotiate" deserves more development, including at least one precedent where this strategy succeeded or failed. Similarly, the "stack crítico soberano" (Pillar 2), requiring sovereign compute capacity for critical functions, has significant cost and feasibility dimensions that would need to be addressed for a real legislative agenda.

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  2. Interesting paper that carries out a solid analysis and makes proposals to respond to the AI regulatory gap and the dependency to foreign AI producers of Mexican stakeholders. Your statement "In exchange of computing, Mexico hands in its national data and its markets..." is, sadly, an accurate description of the current situation which shows why your work is urgent and very relevant.

    Moving forward, you may want to consider:

    * The concepts of MAD and MAIM are based on the military domain and are problematic for various reasons. It would be useful to explain why the authors chose to use these are as analytical framework. There are others which also aim to explain and respond to the challenges of the so-called "Middle Powers", and of global South/global majority countries; so it would be useful to explore those and explain your choices in this regard.

    * Incorporate some discussion of how human rights are affected by lack of legislation and current AI-related harms already happening without accountability, including upstream harms ones such as those of the data centers (case of Querétaro, among others). It would be very useful and more impactful to ground more the proposals from an AI Safety perspective on the actual impacts. Reach out the human rights defenders doing work in this area.

    * There is a wealth of research on decoloniality in the framework of artificial intelligence, reviewing it could be useful to further explain the situation and risks faced by countries with dependency to others for their computing power; and strategies to respond to them from that perspective.

    * Your paper may benefit from at least mentioning the strategies that some tech companies engage in to try to avoid or postpone the adoption of any AI in some countries. It would useful to add this because while many legislative gaps result from national processes, the fact is that industry lobbying is a major factor (for all countries, not only ours) that needs to be made visible and tackled, even in the framework of AI Safety.

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Cite this project

@misc{pinelo2026proteccion,
  title = {{Protección de la soberanía: un marco empírico de vulnerabilidad y un escudo regulatorio para la IA en una potencia media}},
  author = {Max Pinelo and Pilar Moncada},
  year = {2026},
  month = jun,
  note = {Submitted to Global South AI Safety Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/proteccin-de-la-soberana-un-marco-emprico-de-vulnerabilidad-y-un-escudo-regulatorio-para-la-ia-en-una-potencia-media-mpmm}},
  url = {https://apartresearch.com/sprints/projects/proteccin-de-la-soberana-un-marco-emprico-de-vulnerabilidad-y-un-escudo-regulatorio-para-la-ia-en-una-potencia-media-mpmm}
}

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