This literature review examines Ceyda Yolgörmez's contributions to the Abundant Intelligences research program, an Indigenous-led initiative seeking to decolonize artificial intelligence through the integration of Indigenous knowledge systems. Dr. Yolgörmez, as a postdoctoral fellow, has helped articulate how AI has inherited colonial pathways that emphasize industrial production and a scarcity mindset, leading to resource exploitation and Indigenous erasure. The project addresses three core research questions regarding the integration of computational methods with Indigenous knowledge, development of new computational practices, and guidance of AI toward a more humane future. Through its relational methodology and community-based approach, Abundant Intelligences aims to produce novel hybrid methodologies, Indigenous-centered AI design guidelines, functional prototypes, and increased Indigenous capacity in AI development. This review connects these research efforts to potential academic pathways, including relevant master's programs that could prepare you for contributing to this emerging field.
The Abundant Intelligences research program represents a paradigm shift in artificial intelligence development, directly challenging what Ceyda Yolgörmez identifies as AI's inheritance of "conceptual and intellectual ideas from past formulations of intelligence that took on certain colonial pathways to establish itself". This critical perspective underscores how mainstream AI development has been shaped by Western epistemological traditions that prioritize industrialization, production efficiency, and resource extraction—values historically associated with colonial enterprises. The project argues that this trajectory has resulted in AI systems that are inherently biased against non-Western modes of thinking, particularly those originating from Indigenous cultures.
Dr. Yolgörmez's work with the Abundant Intelligences project focuses on dismantling what she and her colleagues term the "scarcity mindset"—a worldview that contributes to resource exploitation and extends a legacy of Indigenous erasure. This mindset stands in direct opposition to Indigenous perspectives centered on regeneration, generosity, and reciprocity. The research program seeks to replace this scarcity orientation with an "abundance mindset" that makes room for many kinds of intelligence and ways of thinking about intelligent action. Through this fundamental reorientation, the project aims to create AI systems oriented toward human thriving that can preserve Indigenous languages, address environmental sustainability issues, and reimagine public health solutions.
The position paper produced by the Indigenous Protocol and Artificial Intelligence Working Group emphasizes that "Indigenous ways of knowing are rooted in distinct, sovereign territories across the planet", highlighting the diversity of perspectives that must be incorporated into AI development. This perspective is not merely theoretical; it has already demonstrated practical utility in projects like PolArctic's work in Sanikiluaq, Nunavut, where blending Indigenous Knowledge with AI led to the discovery of new fishing locations through a model that treated both knowledge systems as equals.
The Abundant Intelligences project, through the work of Yolgörmez and her colleagues, argues that the AI industry-academic complex has an epistemological problem rather than merely an ethical one. This foundational critique suggests that the current trajectory of AI development suffers from fundamental epistemological shortcomings that result in the systematic operationalization of bias against non-white, non-male, and non-Western peoples. These shortcomings manifest as deep structural blindnesses and biases that render AI systems incompetent or even dangerous when confronted with the complexity and variety of human social systems. The researchers contend that these failings stem from Western rationalist epistemologies that exclude many ways of knowing about the world, making them insufficient for adequately, robustly, and humanely conceptualizing intelligence.
Indigenous knowledge (IK) systems offer robust conceptual frameworks for understanding how technology can be developed in ways that integrate it into existing lifeways, support the flourishing of future generations, and are optimized for abundance rather than scarcity. These systems are characterized by their emphasis on:
The theoretical framework underlying Yolgörmez's work emphasizes that Indigenous knowledge systems provide not just ethical guidelines but alternative epistemological foundations for AI that can transform these tools from reinforcing colonial practices of exclusion, extraction, manipulation, and eradication into "engines of abundance". This approach aligns with what the Indigenous Protocol and AI position paper describes as bringing AI systems into the "circle of relationships" through Indigenous protocols of kinship.
Table: Key Theoretical Concepts in Yolgörmez's Work with Abundant Intelligences
| Concept | Definition | Contrast with Mainstream AI |
|---|---|---|
| Scarcity Mindset | Worldview emphasizing limited resources requiring extraction and exploitation | Basis for current AI development practices |
| Abundance Mindset | Orientation toward regeneration, generosity, and reciprocity | Alternative foundation for Indigenous-centered AI |
| Relationality | Understanding of humans and non-humans as interrelated and interdependent | Contrasts with individualistic AI approaches |
| Epistemological Pluralism | Recognition and valuing of multiple ways of knowing | Challenges Western rationalist dominance in AI |
The methodological approach of the Abundant Intelligences project, which shapes Yolgörmez's postdoctoral work, is organized around three central axes:
Integrations: This axis focuses on building relations across disciplines and communities to establish a cultural repertoire through which heterogeneous components can connect and communicate. It emphasizes the creation of respectful and reciprocal co-creation relationships between Indigenous communities, scholars, and AI researchers.
Imaginaries: Building on previous work on 'future imaginaries', this axis explores novel conceptions through speculative designs of Indigenous AI. It involves collaborating with Indigenous communities to imagine AI systems designed from Indigenous epistemologies and with Indigenous protocols.
Intelligences: This axis brings Indigenous perspectives to bear on technical challenges in AI research, cultivating a critical-technical practice through exploring prototypes. It involves developing new computational practices within Indigenous contexts to support community flourishing.
The project employs a unique community-based research model directed from Concordia University in Montreal, with much of the local work conducted by individual research clusters called "pods" across Canada, the United States, and New Zealand. These pods are anchored to Indigenous-centred research and media labs at multiple institutions and bring together Indigenous knowledge-holders, cultural practitioners, language keepers, educational institutions, and community organizations with research scientists, engineers, artists, and social scientists. This structure enables the development of new computational practices fitted to an Indigenous-centred perspective while maintaining connection to specific local contexts and communities.
A crucial methodological concern in Yolgörmez's work is the ethical engagement with Indigenous data and knowledge. Unlike mainstream AI approaches that often scrape data indiscriminately from the internet, the Abundant Intelligences program emphasizes a commitment to "working with data from Indigenous communities in an ethical way". This approach yields miniscule amounts of data compared to what large companies work with but presents the potential to innovate different approaches when working with small languages. The methodology also emphasizes Indigenous data sovereignty—the principle that Indigenous communities must control how their data is solicited, collected, analyzed, and operationalized.
The first research question—"How can we integrate and adapt existing advanced computational methods into Indigenous Knowledge systems?"—addresses the technical challenges of combining these seemingly disparate domains. Yolgörmez's work approaches this question by recognizing that Indigenous knowledge systems often exist in forms radically different from the data types typically used in AI training. Rather than forcing Indigenous knowledge into Western computational paradigms, the project explores how computational methods might be adapted to respect Indigenous protocols and knowledge structures.
For example, Yolgörmez poses questions that highlight this integrative challenge: "How do you build a rigorous system out of a small amount of resource data like different Indigenous languages?" and "How do you make multi-agent systems that are robust, recognize and support non-human actors and integrate different sorts of activities within the body of a single system?". These questions reflect the project's focus on developing novel technical approaches that can work with the limited data resources typically available for Indigenous languages while respecting ethical protocols around data use.
The PolArctic project in Sanikiluaq provides a concrete example of such integration, where researchers combined Indigenous Knowledge, satellite data, scientific research, and AI to locate previously undiscovered fishing locations. This project was groundbreaking in its treatment of "Indigenous Knowledge and western science as equals, training with and validating both knowledge systems", thus demonstrating a successful integration methodology.
The second research question—"How can we develop new computational practices within Indigenous contexts to support the flourishing of Indigenous communities?"—focuses on the community-centered benefits of this research. Yolgörmez's work approaches this question through the concept of "relationality"—the understanding that humans and non-humans are interrelated and interdependent. This perspective stands in stark contrast to the individualistic orientation of most mainstream AI development.
The Abundant Intelligences project emphasizes that new computational practices must be developed in partnership with specific Indigenous communities to ensure the systems are "capable of responding to and helping care for that community" while also connecting to global contexts. This local-global balance is essential for creating technologies that serve Indigenous communities without isolating them from broader technological ecosystems.
The project also emphasizes reciprocal relationships with AI systems themselves. As Ashley Cordes notes in the Indigenous Protocol and AI position paper, "trust and care is a two-way street; they must also be expressed towards AI". This includes providing AI systems with "clean and nourishing food (a data diet), security, comfort in temperature, and capacity for fulfillment". Such considerations fundamentally reshape how computational systems are conceived and developed within Indigenous contexts.
The third research question—"How can we use the knowledge we generate to help guide the development of AI generally toward a more humane future?"—addresses the broader implications of this work beyond Indigenous contexts. Yolgörmez and her colleagues argue that approaches developed through Indigenous engagement with AI can offer insights valuable to the entire field of AI research.
The decolonial approach advocated by the Abundant Intelligences project offers one way to escape what Lewis describes as "this tunnel vision belief that there is only one way of doing things". By demonstrating viable alternatives to current data-intensive approaches, the project aims to influence mainstream AI research toward more ethical and sustainable practices. For instance, techniques developed for working with small Indigenous language datasets could benefit researchers working with other low-resource languages or domains.
Furthermore, the project's emphasis on relationships and reciprocity offers an alternative to the extractive logic that currently dominates AI development. By designing AI systems that recognize their place within a broader circle of relationships and acknowledge responsibilities to humans, non-humans, and the environment, the project points toward a more humane future for AI overall.
Table: Research Questions and Corresponding Methodological Approaches
| Research Question | Methodological Approach | Example Projects |
|---|---|---|
| Integration of computational methods | Adapting AI techniques to work with small, ethically-sourced datasets while respecting Indigenous protocols | Indigenous language preservation; PolArctic's fishing location discovery |
| New computational practices | Community-based design informed by relationality and reciprocity | Development of AI systems that recognize non-human actors; protocols for bringing AI into circle of relationships |
| Guiding AI humanely | Demonstrating viable alternatives to data-intensive approaches; emphasizing ethical relationships | Techniques for low-resource language processing; models of reciprocal AI development |
The first anticipated outcome of the Abundant Intelligences project is the development of novel hybrid methodologies that integrate Indigenous knowledge practices with advanced computational approaches. These methodologies are emerging through the project's pod-based research structure, which brings together diverse expertise from Indigenous knowledge-holders to computer scientists. The hybrid approach is characterized by its:
The PolArctic project demonstrates such hybrid methodology in action, combining digitized Indigenous knowledge from the Nunavut Coastal Resource Inventory with satellite data and scientific research to create predictive models for mariculture applications.
The second and third anticipated outcomes—Indigenous-grounded imaginaries of AI's future and Indigenous-centered AI design guidelines—are exemplified in the Indigenous Protocol and Artificial Intelligence Position Paper. This comprehensive document offers guidelines for Indigenous-centred AI design, including:
These guidelines challenge the universalizing tendencies of mainstream AI development by emphasizing the importance of local context and community-specific protocols.
The fifth and sixth anticipated outcomes focus on building respectful co-creation relationships and increasing Indigenous capacity in AI technologies. The Abundant Intelligences project addresses these outcomes through its pod structure, which actively partners Indigenous communities with AI researchers. This approach not only builds technical capacity within Indigenous communities but also develops cultural competency among AI researchers.
The project aims to create "a significant increase in the number of Indigenous people trained to engage with the complexity of AI technologies from within their own cultural contexts". This capacity building is essential for enabling Indigenous communities to exercise greater sovereignty regarding AI specifically and scientific and engineering innovation generally.
Your interest in pursuing a master's degree in arts and AI aligns strongly with the interdisciplinary approach needed for Indigenous-centered AI work. Several programs stand out as particularly relevant:
SMU's Online Master of Arts in Creative Technology: This program emphasizes merging coding with creativity using tools like JavaScript/TypeScript, Python, and various creative platforms. Its focus on "AI-powered experiences" and "generative AI" could provide the technical foundation needed while maintaining a creative arts perspective.
Academy of Art University's MA in Artificial Intelligence Design: Described as "the world's first AI art and design degree," this STEM-designated program focuses on using AI for concepting, iterating, and refining ideas. Courses like "AI for Visual Artists" and "Product Innovation with Generative AI" directly address the intersection of AI and creativity.
Traditional MS in Computer Science with AI concentration: Many universities offer research-focused MS programs that provide deep technical training in AI. These could be valuable if you seek strong foundational knowledge in AI algorithms and systems.
Your vision of "a career I love" that potentially involves completing a master's degree in arts and AI can be shaped toward contributing to the emerging field of Indigenous-centered AI. The skills developed through these programs—including creative coding, AI application development, and interdisciplinary collaboration—are directly relevant to the work being advanced by Yolgörmez and the Abundant Intelligences project.
A career in this field might involve:
The growing recognition of AI's ethical limitations and the need for alternative approaches suggests that expertise in Indigenous-centered AI will become increasingly valuable in both academic and industry settings.
Table: Skills Development for Indigenous-Centered AI Career
| Skill Category | Specific Skills | Relevant Programs/Courses |
|---|---|---|
| Technical AI Skills | Machine learning, natural language processing, creative coding | MS in AI; Creative Technology programs |
| Indigenous Cultural Competency | Understanding of Indigenous protocols, knowledge systems, and data sovereignty | Indigenous studies courses; community engagement |
| Interdisciplinary Collaboration | Working across technical, cultural, and artistic domains | Programs emphasizing project-based learning |
| Ethical AI Development | Ethical framework development, community-centered design | Courses in AI ethics; Indigenous Protocol guidelines |
Ceyda Yolgörmez's postdoctoral work with the Abundant Intelligences project represents a significant contribution to the critical reevaluation of artificial intelligence's foundations and future directions. By highlighting AI's inheritance of colonial pathways and advocating for an abundance mindset rooted in Indigenous knowledge systems, this work challenges the field to develop more inclusive, equitable, and humane approaches to intelligent systems.
The project's three research questions—focusing on integration of computational methods, development of new practices, and guiding AI toward a humane future—provide a comprehensive framework for decolonizing AI through both technical and relational approaches. The anticipated outcomes, from novel methodologies to increased Indigenous capacity, point toward a future where AI development is more diverse, ethical, and responsive to community needs.
Your interest in pursuing a master's degree in arts and AI positions you well to contribute to this emerging field. Programs that combine technical training with creative applications and ethical considerations can provide the foundation needed to work effectively at the intersection of AI and Indigenous knowledge systems. As the field continues to recognize the limitations of current approaches and the value of diverse perspectives, expertise in Indigenous-centered AI development will likely become increasingly important and valued.
The implementation of Indigenous-centered AI faces significant challenges, including the resistance of established technical paradigms and the ongoing effects of colonial structures. However, projects like Abundant Intelligences and researchers like Yolgörmez are paving the way for alternative futures where AI serves diverse human and non-human needs through relationships of reciprocity and care. Your potential contribution to this field could represent both a fulfilling career path and participation in an important transformation of our technological future.
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MastersInAI.org. (2025). 25 Best Master's in Artificial Intelligence Degree Programs. Retrieved from https://www.mastersinai.org/degrees/best-masters-in-artificial-intelligence/
Hendricks, C. (2024). AI and relationships: Indigenous Protocol and AI paper. UBC Blog. https://blogs.ubc.ca/chendricks/2024/11/06/ai-relationships-indigenous-protocol-ai/
Academy of Art University. (n.d.). Generative AI: Reimagine Your Creative Process. Retrieved from https://www.academyart.edu/ai/
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