FINAL WORD
Can AI exist in Africa without African data?
Without African data, AI systems in Africa are incomplete, especially in sectors like education where students are often taught in languages like English or French. Addressing these gaps with African data is crucial for building inclusive and credible AI systems says Abbie Phatty-Jobe at Caribou Digital.
Artificial Intelligence is a transformative force across industries globally. However, its success and ethical deployment rely on a crucial building block: data. In Africa, AI holds immense potential to address key challenges such as healthcare access, food security, and education.
To harness this potential, it is vital to focus on collecting, integrating, and utilising African data. African data must play a central role in shaping AI solutions that are equitable and globally relevant.
The current global AI ecosystem relies heavily on data from Western countries, leaving African contexts underrepresented. This imbalance perpetuates systemic inequalities and biases, hindering the development of equitable AI. For example, AIpowered agricultural tools often rely on datasets for non-African crops, neglecting indigenous ones like cassava, sorghum, and millet, which limits their ability to address local pests and diseases effectively and utility for farmers.
Recent controversies around AI bias have shown the importance of diversity in training data. Africa’ s rich variety of languages presents a challenge, as many AI models are trained using English databases. Many African languages are oral, with limited digital text, to feed into natural language processing models. This lack of representation in AI systems exacerbates existing disparities and marginalises communities. Without African data, AI systems are incomplete, especially in sectors
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