AI and Indigenous Communities: Ethical Dilemmas and Educational Challenges
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,052 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,030 | 0,033 |
| Communication savante | 0,017 | 0,014 |
| Science ouverte | 0,004 | 0,017 |
| Intégrité de la recherche | 0,012 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».