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Sprint projectJun 22, 2026Colombia

Marco ético para IA aplicada a la preservación lingüística Guna de Panamá

Ana María González Aldana, Kelvin Alvarado, Mariana Zuluaga Abril, Kelvin Alvarado, Mariana Zuluaga Abril · Team CREA TEAM

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

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Report: Marco ético para IA aplicada a la preservación lingüística Guna de Panamá

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This document aims to develop an ethical, responsible, and participatory framework (CREA) for the application of Artificial Intelligence in the preservation and use of the Guna language of Panama. This need arises from technological inequalities between rural and urban areas, which limit the Guna community in its communication with the rest of the population and in its access to essential services, such as medical consultations that currently depend on an external translator.

The final outcome of this project is the adaptation of the CREA framework, consisting of its conceptual pillars and implementation guidelines for the development of AI technologies aimed at language preservation. To achieve this, a qualitative methodology based on document review and the analysis of existing AI ethical frameworks was used, contrasted with principles of Indigenous rights, data sovereignty, and natural language processing.

As a result of the project, it was identified that it is necessary to establish concrete principles to guide technological development in Indigenous contexts. Therefore, the CREA framework, together with its corresponding pillars, is proposed as a response to this need. It is concluded that these guidelines constitute a solid and replicable foundation for the ethical design of AI technologies for under-resourced languages, and their pilot implementation with the Guna community is recommended in future phases.

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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 is an innovative and culturally grounded framework that excels at identifying local harms, community sovereignty concerns, and emerging threats such as voice cloning. The strategic vision behind the project is particularly strong. A valuable next step would be to translate these concepts into practical evidence through data engineering pilots capable of testing the proposed consent mechanisms within real training pipelines.

  2. This is a thoughtful contribution that brings an important and underrepresented community into the AI safety conversation. The problem is well chosen, the motivating argument is compelling, and the effort to ground the framework in concrete, actionable provisions with named responsible actors gives it a practical dimension that purely theoretical proposals often lack. The inclusion of a community member as co-author also adds a layer of legitimacy that is not common in this kind of work and speaks well of the team's commitment to the values the framework itself promotes.

    Where the paper has room to grow is in the consistency between some of its methodological claims and what the document actually demonstrates. There are moments where the analytical ambition reaches slightly ahead of the evidence presented, and tightening that connection would make the overall argument considerably stronger. Additionally, a more explicit engagement with some of the internal tensions in the governance model, particularly around the relationship between community autonomy and external institutional actors, would elevate the conceptual depth of a proposal that is otherwise well constructed. These are natural areas to develop in future iterations rather than fundamental weaknesses.

    On the presentation side, a careful revision pass would benefit the paper noticeably. There are figure numbering inconsistencies, a few truncated sentences, and some minor grammatical irregularities that interrupt an otherwise readable flow. None of these detract from the substance of the ideas, but addressing them would help the work communicate with the clarity and polish its ambition deserves. The research direction is genuinely valuable and the foundation here is strong enough to support a more developed version that could serve as a real reference for similar efforts across the region.

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Cite this project

@misc{aldana2026marco,
  title = {{Marco ético para IA aplicada a la preservación lingüística Guna de Panamá}},
  author = {Ana María González Aldana and Kelvin Alvarado and Mariana Zuluaga Abril and Kelvin Alvarado and Mariana Zuluaga Abril},
  year = {2026},
  month = jun,
  note = {Submitted to Global South AI Safety Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/marco-tico-para-ia-aplicada-a-la-preservacin-lingstica-guna-de-panam-6uxw}},
  url = {https://apartresearch.com/sprints/projects/marco-tico-para-ia-aplicada-a-la-preservacin-lingstica-guna-de-panam-6uxw}
}

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