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March 7, 20260 citationsOpen Access

Indigenous Knowledge Systems Integration into AI Development in West Africa Contextualized for Kenya's Digital Transformation

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OKOxiraj KinyanjuiKNKamau NgugiKNKiplagat Ngugi

Key Points

  • To explore how Indigenous Knowledge Systems (IKS) can be integrated into AI development in Kenya to support digital transformation.
  • Qualitative comparative analysis of AI projects incorporating IKS from West Africa.
  • Preliminary survey of Kenyan AI developers regarding the use of IKS in their models.
  • Focus on healthcare applications and associated challenges.
  • Over 40% of Kenyan AI developers have integrated IKS into their models.
  • In healthcare applications, 55% of developers utilized IKS.
  • Integration of IKS leads to culturally sensitive and sustainable technological solutions.

Abstract

Indigenous Knowledge Systems (IKS) in West Africa are repositories of traditional wisdom and practices that have shaped agricultural techniques, medicine, and social structures for generations. The study employs a qualitative comparative analysis of existing AI projects that incorporate traditional knowledge systems from various West African countries. A preliminary survey revealed that over 40% of Kenyan AI developers have incorporated IKS into their models, particularly in healthcare applications where the proportion is as high as 55%. The integration of IKS into AI can lead to more culturally sensitive and sustainable technological solutions, though challenges related to data privacy and ethical considerations remain. Policy makers should encourage collaboration between traditional knowledge holders and tech developers to ensure equitable benefits from AI applications. Indigenous Knowledge Systems, Artificial Intelligence, Digital Transformation, Kenya Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.

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Cite This Study

Kinyanjui et al. (2008) studied this question.

synapsesocial.com/papers/69abc2615af8044f7a4ebf10https://doi.org/10.5281/zenodo.18881613
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