Integrating Indigenous Knowledge Systems into AI Development in West Africa: A Methodological Approach in Benin

This study examines the integration of Indigenous Knowledge Systems (IKS) into Artificial Intelligence (AI) development in Benin, a West African country known for its rich cultural heritage and diverse ethnic groups. A mixed-methods approach combining qualitative interviews, quantitative surveys, and ethnographic observations was employed to gather data from local communities and stakeholders in Benin. Statistical models were used to analyse the integration of IKS into AI systems, employing a Structural Equation Model (SEM) for predictive analysis. The SEM revealed significant correlations between traditional ecological knowledge and AI model performance on environmental prediction tasks with a confidence interval of ±5%. This study has established a methodological foundation for integrating IKS into AI, particularly in the context of environmental monitoring in Benin. Stakeholders should be encouraged to participate actively in the development and validation of AI models incorporating IKS. Continuous feedback loops between traditional knowledge holders and AI developers are essential for model refinement. 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.