Integrating Indigenous Knowledge Systems into AI Development in West Africa
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2000
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| _version_ | 1866902179850223616 |
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| author | Matope, Emmerson Mutitiwa, Chido Chipungutya, Tinashe Chikodzi, Edzai |
| author_facet | Matope, Emmerson Mutitiwa, Chido Chipungutya, Tinashe Chikodzi, Edzai |
| contents | <p>This study addresses a current research gap in Computer Science concerning Integrating Indigenous Knowledge Systems into AI Development in West Africa in Zimbabwe. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Integrating Indigenous Knowledge Systems into AI Development in West Africa, Zimbabwe, Africa, Computer Science, working paper This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. 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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18713636 |
| institution | Zenodo |
| language | eng |
| publishDate | 2000 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Integrating Indigenous Knowledge Systems into AI Development in West Africa Matope, Emmerson Mutitiwa, Chido Chipungutya, Tinashe Chikodzi, Edzai Sub-Saharan AfricanComputing IndigenousExpertise LinguisticAI CulturalProgramming DataHarvesting Socio-TechnicalIntegration <p>This study addresses a current research gap in Computer Science concerning Integrating Indigenous Knowledge Systems into AI Development in West Africa in Zimbabwe. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Integrating Indigenous Knowledge Systems into AI Development in West Africa, Zimbabwe, Africa, Computer Science, working paper This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. 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.</p> |
| title | Integrating Indigenous Knowledge Systems into AI Development in West Africa |
| topic | Sub-Saharan AfricanComputing IndigenousExpertise LinguisticAI CulturalProgramming DataHarvesting Socio-TechnicalIntegration |
| url | https://doi.org/10.5281/zenodo.18713636 |