Integrating Indigenous Knowledge Systems into AI Development in Cameroon: A Replication Study

This study builds upon previous research by exploring how indigenous knowledge systems can be integrated into artificial intelligence (AI) development in Cameroon, a West African country rich in diverse cultural and linguistic traditions. A mixed-methods approach was employed, combining qualitative interviews with quantitative surveys among local communities and AI developers in Cameroon. Data were collected through structured questionnaires and semi-structured interviews, ensuring a balanced representation of indigenous knowledge systems and technological advancements. The analysis revealed that integrating indigenous knowledge systems into AI development can lead to more culturally sensitive and contextually relevant applications, with 45% of respondents indicating improved user engagement due to culturally tailored features. Specific themes emerged around the necessity for localized data sets and collaborative research models among AI developers and traditional knowledge holders. This replication study confirms the initial findings that integrating indigenous knowledge systems into AI development enhances its relevance and effectiveness in local contexts, particularly in terms of cultural sensitivity and user engagement. Based on these results, recommendations include fostering cross-disciplinary collaborations between technologists and traditional knowledge holders to better address local needs and ensure culturally appropriate AI solutions are developed. 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.