Indigenous Knowledge Systems Integration into AI Development in West Africa: A Moroccan Perspective

Indigenous Knowledge Systems (IKS) in West Africa, particularly Morocco, are rich repositories of traditional wisdom and practices that have been developed over centuries through interaction with natural environments and social structures. A mixed-methods approach was employed, combining qualitative interviews with semi-structured questionnaires to gather data from indigenous practitioners and stakeholders. Quantitative analysis of AI model performance metrics was conducted using a linear regression model to assess the impact of IKS integration on accuracy and efficiency. The findings indicate that incorporating traditional ecological knowledge (TEK) into AI models for resource management applications resulted in an average improvement of 15% in prediction accuracy, with reductions in error rates by up to 20% when compared to standard machine learning algorithms. This study provides empirical evidence supporting the potential benefits of integrating IKS into AI development. The success demonstrates a promising pathway for leveraging local knowledge to improve technological solutions tailored to specific regional needs. Further research should focus on replicating these findings across different domains and exploring broader applications, while also considering ethical considerations related to data ownership and privacy in indigenous contexts. Indigenous Knowledge Systems, AI Development, Morocco, Linear Regression Model, TEK Integration Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.