Indigenous Knowledge Systems (IKS) are deeply rooted in the cultural contexts from which they emerge. They encompass a wide range of knowledge, from traditional ecological knowledge to cultural practices and beliefs. AI systems used to preserve and disseminate IK should be designed in a way that respects and acknowledges these unique cultural contexts. This includes understanding the cultural protocols around knowledge sharing and ensuring that AI does not infringe upon these protocols.

AI systems are fundamentally data-driven, meaning that they learn and make predictions based on the data that is fed into them. This data can come from a variety of sources and can encompass a wide range of knowledge, including Indigenous Knowledge Systems (IKS). However, the way AI systems are currently trained often leaves out Indigenous knowledge, as these systems are predominantly trained on Western knowledge. This can result in AI systems that lack a holistic understanding of the world and that do not adequately represent or respect Indigenous knowledge (Dower, 2023).

One way to address this issue is to involve Indigenous communities in the training of AI systems. For example, a project at the University of Hawaii, funded by Canada’s New Frontiers in Research Fund, is working to develop AI using Indigenous knowledge. In this project, teams of students and faculty collaborate with local members of Indigenous communities to develop their AI. The goal is to train the AI so that it has a more holistic understanding of the world and the work it commands (Dower, 2023).

Another project, conducted by PolArctic in Sanikiluaq, an Inuit community in Nunavut, Canada, integrated traditional Indigenous Knowledge, scientific data, and remote sensing techniques to locate previously undiscovered fishing locations. This project was the first to combine Indigenous Knowledge, satellite data, scientific research, and AI to address a problem caused by the climate crisis. The AI model treated Indigenous Knowledge and western science as equals, training with and validating both knowledge systems (PolArctic, 2023).

These projects highlight the potential for AI to consider Indigenous knowledge in its outputs, but this requires active involvement and input from Indigenous communities. It also requires careful consideration of ethical issues, such as data sovereignty and how Indigenous knowledge should be used and disseminated ethically (Dower, 2023; PolArctic, 2023).

Inclusion of Indigenous Peoples in AI Development

The development and implementation of AI systems for IK preservation should not be a top-down process. Instead, it should involve the active participation of Indigenous peoples. It is not only a matter of respect and representation but also a means of empowerment and capacity building. The active participation of these communities can ensure that AI systems accurately represent their knowledge and respect their cultural values, while also providing them with the skills and understanding to leverage these technologies for their benefit.

One way to involve Indigenous peoples in AI development is through participatory design, a method that involves end-users in the design process to ensure that the product meets their needs and is usable. In the context of AI for Indigenous Knowledge (IK) preservation, this could involve Indigenous communities in defining the problem, designing the solution, and testing the AI system. This approach not only ensures that the AI system is culturally appropriate and effective but also provides Indigenous peoples with an understanding of AI and its potential applications (Sanders & Stappers, 2008).

Education is another crucial aspect of this process. By providing Indigenous peoples with training in AI and related technologies, they can better understand how these systems work and how they can be used to preserve and promote their culture. This education can also demystify AI, helping to alleviate any fears or misconceptions about the technology. Furthermore, it can provide Indigenous peoples with valuable skills that can be used in various sectors, contributing to economic development and self-determination (Sanders & Stappers, 2008).

The inclusion of Indigenous peoples in AI development can also have broader societal benefits. By incorporating diverse perspectives and knowledge systems, AI can become more innovative and inclusive. Furthermore, it can contribute to the preservation and promotion of Indigenous cultures, which are often underrepresented and marginalized in mainstream society (Sanders & Stappers, 2008).

Thus, the inclusion of Indigenous peoples in AI development is not just about creating culturally appropriate and effective AI systems but also about empowering these communities, promoting self-determination, and contributing to societal diversity and innovation. By involving Indigenous peoples in the design process and providing them with education in AI, we can help ensure that they are not just passive recipients of this technology, but active participants in its development and use.

Download Full Paper here : Balancing Technology and Tradition:

Ethical Considerations in Using AI for Indigenous Knowledge Preservation