AI and Indigenous Communities: Ethical Dilemmas and Educational Challenges
Bibliographic record
Abstract
This panel aims to present and discuss five ongoing research projects, inviting feedback to implement modifications that will improve the research instruments and enhance their impact on the population. The first explores the integration of Mapuche ancestral health principles into modern bioethics and public health frameworks, contributing to the 2030 health objectives. Funded by FONIS (SA24I0135), the research is led by Dr Carolina Valdebenito as co-director. We aim to construct a bioethical analytical model based on the Mapuche worldview, offering a culturally sensitive approach to health, illness, and death. By identifying the ethical principles guiding Mapuche health practices, we compare them with Western bioethical models, proposing recommendations for inclusive and culturally relevant health policies. In addition to these bioethical concerns, the panel highlights the application of artificial intelligence (AI) in rural and Indigenous communities. Dr. Cristian Olivares's research focuses on AI's role in forest fire prevention in Chile. His work demonstrates the potential for AI to help protect the environment in rural and Indigenous areas, while also preventing such events. Olivares's findings emphasize the importance of adapting technology to the cultural context of these communities to ensure its effectiveness. Dr. Martha Vidal’s work on AI integration in rural education in Chile further enriches the discussion. Vidal's research explores how AI can enhance educational outcomes in rural and Indigenous schools by addressing the unique challenges faced by these communities, such as geographic isolation and linguistic diversity. Her work underscores the necessity of culturally sensitive approaches when applying AI in educational contexts. Dr. Fabienne Martin-Juchat’s research on preserving Indigenous knowledge, particularly related to traditional medicine, forms an important part of the panel. Her work focuses on the role of digital tools in conserving and transmitting traditional healing practices within Indigenous communities in Canada. Martin-Juchat’s studies explore the intersection of technology and cultural preservation, showing how AI can help protect and transmit invaluable knowledge while fostering intercultural understanding and respect. Her work emphasizes the need for a balance between modern technology and traditional knowledge to ensure the survival of these practices in an increasingly globalized world. Finally, the panel will discuss the Atacameño people’s struggle for cultural preservation amid the extractive industries in northern Chile. The article, Atacameños and their Cultural Identity in an Extractivist Framework: Reflections and Challenges for Participatory and Inclusive Education, co-authored with Ulises Cárdenas, explores the impacts of mining on the Atacameño community, particularly in terms of education and identity. This work contributes to the broader dialogue on how extractive industries affect Indigenous communities and their need for inclusive, culturally respectful education systems. Through these interconnected discussions, the panel offers a comprehensive approach to integrating Indigenous perspectives into modern health and educational frameworks, ultimately contributing to the creation of healthier, more inclusive communities in line with the 2030 health objectives
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.033 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".