AI for Indigenous-Led Nonprofits: Cultural Preservation, Community Engagement, and Language Revitalization
Indigenous-led nonprofits face a unique opportunity, and responsibility, when considering AI implementation. While AI offers powerful tools for language revitalization, cultural preservation, and community engagement, these technologies were not designed with Indigenous data sovereignty, cultural protocols, or community self-determination in mind. This comprehensive guide explores how Indigenous-led organizations can harness AI's potential while maintaining control over community data, honoring cultural values, and centering Indigenous leadership in technology decisions.
Across Indigenous communities worldwide, nonprofit organizations work to preserve languages spoken by dwindling numbers of elders, maintain cultural knowledge threatened by historical trauma and forced assimilation, strengthen connections among geographically dispersed community members, and support youth engagement with cultural practices and traditional knowledge. These missions face urgent timelines, many Indigenous languages have fewer than 100 fluent speakers, and each passing elder represents irreplaceable knowledge.
Artificial intelligence presents both promise and peril in this context. On one hand, AI technologies offer unprecedented capabilities for language documentation and learning, automated transcription and translation of oral histories, pattern recognition in cultural practices and traditional knowledge systems, scalable community engagement and connection across distances, and preservation of cultural materials in accessible digital formats. Organizations like First Languages AI Reality (FLAIR) are building speech recognition models for over 200 endangered Indigenous languages, demonstrating AI's potential to support language reclamation at scale.
On the other hand, the AI industry's extractive data practices, centralized control, and profit-driven development model directly contradict Indigenous principles of data sovereignty, collective benefit, and community control. In December 2024, the proliferation of AI-generated "how-to" books for learning Abenaki language, containing incorrect translations and non-Abenaki words, illustrated how AI can cause harm when developed without authentic Indigenous participation. These poorly made resources not only mislead learners but also disrespect the language and culture they claim to teach.
This article provides Indigenous-led nonprofits with a framework for evaluating and implementing AI tools in ways that honor community sovereignty, protect cultural knowledge, and advance self-determined goals. Whether you're considering AI for language preservation, community engagement, or operational efficiency, understanding how to maintain Indigenous control over technology decisions and data practices is essential.
The path forward requires neither wholesale rejection of AI nor uncritical adoption. Instead, Indigenous-led nonprofits can leverage these tools strategically, on their own terms, with safeguards that protect community interests and cultural integrity. This balanced approach, grounded in Indigenous data sovereignty principles, enables organizations to benefit from AI's capabilities while maintaining the community control that makes any technology truly useful for Indigenous self-determination.
Understanding Indigenous Data Sovereignty: OCAP® and CARE Principles
Before implementing any AI tool, Indigenous-led nonprofits must ground their approach in Indigenous data sovereignty principles. These frameworks were developed by Indigenous communities specifically to counter extractive data practices and assert Indigenous peoples' rights to govern how information about their communities is collected, used, and shared. Unlike mainstream data privacy frameworks that focus on individual consent, Indigenous data sovereignty recognizes collective rights and responsibilities.
Two primary frameworks guide Indigenous data governance: OCAP® (Ownership, Control, Access, and Possession) developed by First Nations in Canada, and the CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics) created through collaboration among Indigenous data sovereignty networks in Aotearoa New Zealand, Australia, and the United States. Understanding these frameworks helps Indigenous-led nonprofits evaluate whether AI tools and vendors respect Indigenous rights and support community self-determination.
OCAP® Principles
First Nations standards for Indigenous data governance in Canada
Ownership
Indigenous communities own their information collectively, just as individuals own personal information. This means language recordings, cultural knowledge, community data, and oral histories belong to the community, not to researchers, technology vendors, or nonprofits as institutions.
Control
Indigenous communities and Nations seek control over research data and its management at all stages: collection, storage, analysis, and dissemination. When AI processes community data, Indigenous peoples must control decisions about how that processing occurs and for what purposes.
Access
Indigenous communities must be able to access data about themselves and make or participate in decisions regarding who else can access it. This includes accessing AI models trained on community data and understanding how those models function.
Possession
Physical control of data should rest with the Indigenous community or an Indigenous-controlled steward. Cloud-based AI services that store data on corporate servers create tension with this principle, requiring careful evaluation and contractual protections.
CARE Principles
Global Indigenous data governance framework
Collective Benefit
Data ecosystems should be designed to enable Indigenous peoples to derive benefit from the data for inclusive development and innovation. AI implementations must serve community-defined goals, not just organizational efficiency or vendor profits.
Authority to Control
Recognition of Indigenous peoples' rights, interests, and authority in Indigenous data. AI vendors and nonprofit organizations must acknowledge that authority over community data ultimately rests with the Indigenous community, not with external institutions.
Responsibility
Those working with Indigenous data have responsibilities to relationships built on respect, reciprocity, trust, and mutual understanding. AI implementation should strengthen these relationships, not extract value while providing minimal community benefit.
Ethics
Indigenous peoples' rights and wellbeing should be the primary concern at all stages of the data life cycle. When AI creates risks, whether privacy violations, cultural misappropriation, or unintended harms, Indigenous communities' interests take precedence over technological convenience or efficiency.
Implementing these principles in AI contexts requires specific actions. Indigenous-led nonprofits should establish clear governance structures that give communities decision-making authority over AI implementations, not just advisory roles. Contracts with AI vendors must include explicit provisions protecting community data ownership and prohibiting uses that conflict with OCAP® and CARE principles. Technology selections should prioritize solutions that allow on-premises data storage or Indigenous-controlled cloud infrastructure over vendor-controlled systems.
Importantly, adhering to these principles doesn't mean rejecting all cloud-based or commercial AI tools. It means evaluating each tool against sovereignty principles, negotiating appropriate safeguards, and maintaining community control over how the technology is used. Some Indigenous-led organizations have successfully partnered with AI vendors by establishing clear boundaries, strong contracts, and oversight mechanisms that preserve Indigenous authority while leveraging external technical capabilities.
The fundamental question Indigenous-led nonprofits must ask about any AI implementation: Does this strengthen or weaken our community's control over our own information, knowledge, and future? Tools and partnerships that strengthen community control align with sovereignty principles; those that require ceding control to external entities warrant careful scrutiny and potentially rejection, regardless of their technical sophistication or promised benefits.
AI for Language Revitalization: Opportunities and Cautions
Language revitalization represents one of the most promising, and most sensitive, applications of AI for Indigenous communities. With many Indigenous languages facing extinction as elder speakers pass away, time is critically short for documentation, preservation, and transmission to younger generations. AI technologies offer capabilities that previous generations of language advocates could only dream of: automated speech recognition that can transcribe oral recordings, text-to-speech systems that preserve proper pronunciation, translation tools to make language learning more accessible, and interactive chatbots that provide conversational practice when fluent speakers aren't available.
However, language is inseparable from culture, identity, and spiritual practices for many Indigenous communities. Using AI for language work therefore carries profound responsibilities and potential risks. The technology must be implemented in ways that honor the sacred nature of language, respect protocols around who can access and teach certain knowledge, and ensure that language preservation strengthens, rather than commodifies or distorts, cultural transmission.
Promising AI Applications for Language Work
How Indigenous-led nonprofits are using AI to support language reclamation
- Automated transcription of elder interviews and oral histories: Speech recognition tools can convert recorded conversations into text, dramatically reducing the time required for transcription. The First Languages AI Reality initiative has developed speech recognition models for over 200 endangered Indigenous languages, enabling faster documentation of irreplaceable knowledge from elder speakers.
- Text-to-speech systems that preserve pronunciation: AI can generate audio of proper pronunciation even when fluent speakers aren't available, helping learners hear how words should sound. This technology allows language to have a "digital voice" that can support learning across distances and time zones.
- Interactive language learning chatbots: AI conversational agents like Masheli (developed for Choctaw language) provide low-pressure practice opportunities for learners who might feel intimidated speaking with fluent community members. These chatbots can be available 24/7, offering consistent practice opportunities.
- Digital dictionaries and language repositories: Projects like Living Dictionaries create online repositories where community members can collaboratively document languages, with AI assisting in organizing, searching, and connecting related terms and concepts.
- Translation assistance for language learners: AI translation tools can help bridge understanding between Indigenous languages and dominant languages, though with important caveats about accuracy and cultural context that we'll explore below.
- Pattern analysis for language documentation: Machine learning can help identify grammatical patterns, sound systems, and linguistic structures, potentially accelerating the work of language documentation when guided by expert linguists and fluent speakers.
Critical Risks and Limitations of AI for Language Work
Challenges that Indigenous-led nonprofits must address when using AI for language revitalization
Insufficient Training Data Creates Inaccuracies
Most AI language models require vast amounts of text and audio data to function accurately. Many endangered Indigenous languages lack sufficient data for training robust AI systems, leading to errors, mistranslations, and misrepresentations. The December 2024 Abenaki language incident, where AI-generated books contained incorrect translations and non-Abenaki words, illustrates this danger.
Mitigation approach: Always involve fluent speakers in reviewing and correcting AI outputs. Treat AI as a tool to assist human expertise, never as a replacement for authentic language knowledge. Be transparent with language learners about AI limitations and the possibility of errors.
Loss of Cultural Context and Nuance
Indigenous languages often embed cultural knowledge, worldviews, and protocols that direct word-for-word translation cannot capture. AI systems typically lack understanding of cultural context, appropriate usage situations, sacred knowledge restrictions, or the relationships and protocols that govern language transmission.
Mitigation approach: Use AI for mechanical tasks (transcription, basic translation) while ensuring cultural teaching and context remain centered in human relationships. Design AI implementations that support, rather than replace, traditional transmission from elders and knowledgeable community members to learners.
Risk of Cultural Appropriation and Commodification
When Indigenous language data enters commercial AI systems, it may be used to train models that generate profit for technology companies while providing minimal benefit to communities. Language content could appear in applications or contexts that community members find inappropriate, disrespectful, or harmful.
Mitigation approach: Prioritize Indigenous-controlled AI initiatives like FLAIR, or work with vendors who contractually commit to Indigenous data sovereignty principles. Ensure contracts explicitly prohibit using language data to train commercial models or for purposes beyond community-approved uses.
Potential for Disrespecting Sacred Knowledge Protocols
Many Indigenous communities have protocols about who can access, teach, or share certain aspects of language, particularly ceremonial language, sacred terms, or knowledge restricted to specific roles or genders. AI systems that make all documented language universally accessible may violate these protocols.
Mitigation approach: Establish clear governance about what language materials can be digitized and made accessible through AI systems. Some knowledge should remain with authorized knowledge keepers and not enter AI tools, regardless of technological capability. Respect community decisions about boundaries, even when technology makes unrestricted access technically possible.
Dependency on External Technology Infrastructure
Cloud-based AI tools create dependency on corporate technology infrastructure, internet connectivity, and ongoing subscription costs. If a vendor discontinues service, changes pricing, or modifies data practices, communities may lose access to their own language resources.
Mitigation approach: Maintain local copies of all language data and materials. Consider open-source, locally-hosted AI tools when feasible. Build redundancy so that loss of one vendor doesn't mean loss of community language resources. See our article on open source AI for nonprofits for alternatives to commercial platforms.
The most successful AI language revitalization initiatives share common characteristics: they're led by Indigenous technologists and language experts who understand both the cultural and technical dimensions, they involve communities in decisions about what gets documented and how it's shared, they combine AI capabilities with traditional teaching methods and human relationships, they respect protocols and boundaries around sacred or restricted knowledge, and they maintain Indigenous control over language data and its uses.
Organizations like IndigiGenius, Tech Natives, and the Wihanble S'a Center for Indigenous AI demonstrate that Indigenous communities can successfully lead AI development for their own purposes. These initiatives train Native American, Alaska Native, and Native Hawaiian computer science students to preserve Indigenous culture and language using AI, ensuring that Indigenous peoples aren't just subjects of AI language work but leaders in developing the technology on their own terms.
Research has shown that keeping Indigenous languages alive produces benefits beyond cultural preservation, including lower teen suicide rates, improved mental health outcomes, stronger cultural identity, and even better physical health outcomes like lower rates of diabetes and substance abuse. These profound benefits underscore why language revitalization matters and why getting AI implementation right, in ways that honor culture rather than harm it, carries such importance.
AI for Cultural Preservation and Knowledge Management
Beyond language revitalization, Indigenous-led nonprofits are exploring how AI can support broader cultural preservation efforts: organizing and making accessible vast archives of cultural materials, identifying patterns in traditional knowledge systems, connecting dispersed community members with cultural resources, and preserving practices and knowledge from elders before it's lost. These applications offer real value but require thoughtful implementation that respects cultural protocols and community authority.
Traditional knowledge systems represent millennia of observation, experimentation, and wisdom about everything from ecological relationships to healing practices, from governance systems to artistic traditions. This knowledge lives primarily in oral tradition, community practice, and the memories of elders. As these knowledge keepers age, communities face urgent pressure to document and preserve knowledge for future generations. AI tools can assist this work but only when implemented in ways that honor the relational, contextual, and sacred nature of Indigenous knowledge.
Digital Archives and Collections
AI can help Indigenous-led nonprofits organize, catalog, and make searchable large collections of cultural materials, photographs, recordings, documents, and artifacts. Machine learning systems can automatically tag images, transcribe audio, and create metadata that helps community members find relevant materials.
Key considerations: Who controls access to archived materials? AI can make collections universally searchable, but some materials may be culturally sensitive or restricted to certain community members. Access control systems must respect these protocols rather than defaulting to open access.
Community-based archives and knowledge repositories built with Indigenous data sovereignty principles demonstrate how to balance preservation with appropriate access. These systems use AI to enhance organization while maintaining community control over who can view, download, or use cultural materials.
Pattern Recognition in Traditional Knowledge
AI excels at identifying patterns across large datasets, a capability that could help analyze traditional ecological knowledge, identify relationships in oral histories, or recognize connections in artistic or cultural practices that might not be immediately obvious to individual researchers.
Critical safeguard: AI pattern recognition should support, not replace, knowledge keepers' expertise and interpretation. Technology may identify correlations, but understanding their meaning and significance requires cultural knowledge and appropriate authority.
When AI analysis produces insights about traditional knowledge, communities must decide how and whether those insights are shared. AI doesn't grant nonprofits or researchers authority to publish or distribute knowledge, that authority remains with the community and designated knowledge keepers.
Community Engagement and Connection
Geographic dispersion challenges many Indigenous communities, with members spread across cities, states, or countries far from traditional territories. AI-powered platforms can help Indigenous-led nonprofits maintain cultural connections across distances through personalized content delivery, virtual cultural events with AI-enhanced translation, connection matching to link elders with youth learners, and community-generated content sharing.
These applications work best when they strengthen existing relationships rather than attempting to replace in-person, face-to-face cultural transmission. Technology should serve as a bridge during times of physical separation, not as a substitute for the embodied, relational nature of cultural practice.
For guidance on building effective community engagement systems, see our article on AI for nonprofit knowledge management, which explores how to organize and share organizational knowledge in accessible ways.
Oral History Preservation
AI transcription and translation tools can accelerate the work of preserving elder interviews, storytelling recordings, and oral histories. What once required hundreds of hours of manual transcription can now be partially automated, freeing up staff time for more complex cultural work.
However, automated transcription of Indigenous languages often produces errors, particularly with languages that have small training datasets. Transcripts always require review and correction by fluent speakers or trained language workers.
Workflow recommendation: Use AI to create initial rough transcripts, then have knowledgeable community members review and correct them. This hybrid approach captures AI's speed benefits while ensuring accuracy and cultural appropriateness. The review process itself becomes an opportunity for language learning and cultural transmission when elders and younger community members work together on corrections.
Cultural preservation AI implementations succeed when they enhance rather than replace traditional knowledge transmission. The goal isn't to put all cultural knowledge into databases that AI can search, it's to use AI as a tool that helps elders and knowledge keepers share their wisdom with the next generation more effectively, to make cultural resources more accessible to community members who seek them, and to preserve knowledge in formats that will outlast individual knowledge keepers while respecting protocols about appropriate access and use.
Indigenous-led nonprofits should approach cultural preservation AI with a clear hierarchy: community protocols and cultural values always take precedence over technological capability or efficiency. When AI makes something technically possible that cultural protocols prohibit, the protocols win. This principle may seem obvious, but technology's seductive promise of comprehensive preservation and universal access can tempt organizations to override cultural boundaries "for the greater good." Resisting this temptation, maintaining community authority over cultural knowledge even when technology enables broader sharing, represents essential respect for Indigenous data sovereignty.
Selecting AI Vendors: Questions for Indigenous-Led Nonprofits
When Indigenous-led nonprofits evaluate AI vendors, the standard vetting process used by other nonprofits isn't sufficient. Beyond typical concerns about functionality, cost, and security, Indigenous organizations must assess whether vendors understand and respect Indigenous data sovereignty, can accommodate community protocols and governance structures, and genuinely commit to collective benefit rather than extractive data practices.
Vendor responses to these questions reveal whether they've thoughtfully considered Indigenous contexts or merely added diversity language to standard marketing materials. Organizations should be wary of vendors who resist questions about data sovereignty or who claim their standard terms already provide adequate protections without specific provisions addressing Indigenous principles.
Essential Questions for AI Vendors
What to ask before partnering with AI technology providers
Data Sovereignty and Ownership
- Are you familiar with OCAP® and CARE principles for Indigenous data governance, and will you commit to honoring them in our partnership?
- Can you provide contractual language confirming that our community maintains complete ownership of all cultural data, language materials, and knowledge that enters your system?
- Will you commit in writing that our community data will never be used to train commercial AI models, shared with other customers, or used for any purpose beyond the specific services we contract?
Community Control and Governance
- Can your system accommodate community-based governance structures where a council or committee, not just organizational staff, must approve certain decisions?
- How can we configure access controls that respect cultural protocols about who may view or use certain materials?
- If our community decides certain materials should be removed or access should be restricted differently, can we make those changes immediately without vendor approval?
Cultural Competency and Respect
- Does your organization have Indigenous employees in leadership roles who influence product development and data practices?
- Have you worked with other Indigenous organizations, and can you provide references who can speak to your cultural competency and respect for sovereignty?
- Are you willing to engage in cultural protocols appropriate to our community (for example, relationship-building before business discussions, or participation in cultural events)?
Data Portability and Exit Strategy
- If we terminate the partnership, can we export all our data in formats we can use with other systems or store locally?
- What happens to our data after contract termination, will it be completely deleted, and how will you verify deletion to our satisfaction?
- Can you guarantee that no remnants of our cultural data remain in your systems, backups, or AI models after we end the relationship?
Pricing and Sustainability
- Do you offer special pricing for Indigenous-led nonprofits that reflects collective benefit principles?
- If our funding situation changes, what options exist to maintain access to our own data and materials?
- Can we negotiate a relationship where the community gains increasing ownership or control of the technology over time?
Indigenous-led nonprofits should prioritize vendors who demonstrate genuine commitment to Indigenous data sovereignty through actions, not just words. Green flags include vendors who readily agree to OCAP®/CARE-aligned contract terms, Indigenous leadership within the vendor organization, track record of respectful partnerships with Indigenous communities, flexible technology that accommodates cultural protocols, transparent data practices with nothing to hide, and willingness to engage in relationship-building appropriate to Indigenous protocols.
Red flags include resistance to data sovereignty questions or dismissal of their importance, claims that standard privacy terms already cover Indigenous concerns without specific provisions, business models reliant on monetizing customer data, inability to guarantee community data won't train commercial models, pressure to move quickly without adequate community consultation, and unwillingness to accommodate cultural protocols in the business relationship.
When evaluating vendors, Indigenous-led nonprofits should involve community members in decisions, not just organizational staff. The "client" in Indigenous data sovereignty isn't the nonprofit organization, it's the community whose data and knowledge are at stake. Meaningful vendor evaluation therefore requires meaningful community participation in the assessment process. For additional perspectives on vendor evaluation and technology decision-making, see our article on the nonprofit leader's guide to AI implementation.
Local AI and Open Source: Building Community Control
For Indigenous-led nonprofits serious about data sovereignty, local AI systems and open source tools deserve strong consideration. Rather than sending community data to commercial cloud services, local AI processes data on computers the organization controls, often on-premises servers or Indigenous-owned infrastructure. This approach directly supports OCAP® principles, particularly Possession (physical control of data) and Control (community authority over data management).
Local AI also provides protection against vendor dependencies, service discontinuation, pricing changes, and policy modifications that could threaten community access to their own data. When the AI runs on your infrastructure using open source software, the community maintains control regardless of external market forces or corporate decisions.
Local AI Tools for Indigenous Nonprofits
Open source and locally-hosted options that support data sovereignty
Small Language Models (SLMs) for Language Work
Tools like Ollama, LM Studio, and GPT4All allow Indigenous-led nonprofits to run AI language models locally on standard computers. While these models may have less capability than large commercial systems, they provide complete data privacy and community control, ideal for sensitive language documentation work where cultural protocols prohibit sending data to external servers.
Federated Learning for Multi-Site Organizations
Indigenous organizations with multiple locations or chapters can use federated learning approaches that allow AI model training across sites without centralizing data. Each location keeps its data locally while contributing to shared model improvement, supporting both collective benefit and local control.
Open Source Speech Recognition
Projects like Mozilla Common Voice and the First Languages AI Reality initiative provide open source speech recognition that Indigenous communities can customize for their languages. Unlike commercial services, these tools allow complete transparency about how they work and full community control over training data.
Synthetic Data for Privacy Protection
When AI requires substantial training data but cultural protocols limit sharing of actual community information, synthetic data techniques can generate artificial training data that preserves statistical patterns without exposing real community information. This advanced approach requires technical expertise but offers a pathway to leverage AI capability while protecting sensitive knowledge.
Local AI isn't without challenges. It requires more technical expertise to implement and maintain than using commercial cloud services. It typically offers less powerful capabilities than the latest commercial models. It demands investment in hardware and infrastructure that organizations control. And it may lack the polished user interfaces and support resources of commercial products.
However, for Indigenous-led nonprofits prioritizing sovereignty over convenience, these trade-offs often make sense. The technical challenges can be addressed through partnerships with Indigenous-led technology organizations, capacity building in community members, collaborative infrastructure sharing among multiple Indigenous nonprofits, or hybrid approaches that use commercial tools for non-sensitive work while keeping cultural data local.
Building Indigenous Technology Capacity
Developing community-controlled technical expertise for long-term sovereignty
The most sustainable approach to Indigenous data sovereignty involves developing Indigenous technical expertise, training community members in AI development, data management, and technology infrastructure so that communities aren't dependent on external experts.
Organizations like IndigiGenius, Tech Natives, and the Wihanble S'a Center for Indigenous AI train Native American, Alaska Native, and Native Hawaiian computer science students to preserve Indigenous culture and language using AI. These initiatives demonstrate that Indigenous communities can lead AI development, not just consume products developed by others.
Indigenous-led nonprofits should consider investing in technical capacity building for community members, whether through scholarships for computer science education, partnerships with Indigenous tech training programs, or mentorship relationships with Indigenous technologists. This long-term investment strengthens community sovereignty far beyond any specific AI implementation. For strategies on building this internal capacity, see our article on building AI champions within your nonprofit.
Conclusion: AI on Indigenous Terms
The question for Indigenous-led nonprofits isn't whether to use AI, it's how to use AI in ways that strengthen rather than undermine community sovereignty, cultural integrity, and self-determination. This requires approaching AI not as a neutral tool but as a technology embedded with particular values, power dynamics, and assumptions that may conflict with Indigenous principles.
Indigenous data sovereignty frameworks, OCAP® and CARE, provide the foundation for evaluating and implementing AI tools appropriately. These aren't aspirational principles to consider when convenient; they're non-negotiable requirements that must shape every technology decision. When vendors, researchers, or even well-meaning nonprofit staff propose AI implementations that would compromise Indigenous control over community data and knowledge, the answer must be no, regardless of promised benefits or technological sophistication.
At the same time, Indigenous-led nonprofits shouldn't feel pressured to reject AI entirely or to accept being left behind in technological development. Indigenous communities have always been innovators and adapters, selectively adopting new tools and technologies in ways that serve community-defined goals while maintaining cultural continuity. AI can be another such tool, powerful, useful, and worthy of adoption when implemented on Indigenous terms.
The path forward combines clear boundaries with creative possibility: establishing firm requirements for data sovereignty and community control, partnering with vendors and technologists who genuinely respect Indigenous authority, prioritizing Indigenous-led AI initiatives and local/open source solutions when feasible, developing Indigenous technical capacity for long-term sustainability, and always centering community protocols, values, and self-determination above technological capability or efficiency.
Indigenous-led nonprofits implementing AI have an opportunity to model for the broader nonprofit sector what technology sovereignty looks like, how organizations can leverage powerful tools while maintaining community control, how to evaluate vendors through a sovereignty lens rather than just a feature checklist, and how to build sustainable technology practices that serve communities rather than extracting value from them.
Language revitalization, cultural preservation, and community engagement work supported by AI can help ensure that Indigenous knowledge, languages, and cultures thrive for generations to come. But only when that AI serves Indigenous self-determination rather than external interests. This is the standard Indigenous-led nonprofits must uphold, and the vision they must work toward, AI as a tool for Indigenous futures, on Indigenous terms, under Indigenous control.
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