Forgotten African AI Narratives and the future of AI in Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ancient and contemporary imaginative thoughts, stories, literary works, and beliefs about intelligent machines or otherwise known as AI narratives influence the development, implementation and governance of AI. Responsible AI therefore requires the understanding of these narratives. However, in the global AI narratives discourse, narratives of AI from Africa are missing or are often forgotten. Potentially, this has implications for how AI is or will be designed, deployed and regulated in Africa. This paper presents insights into our understanding of the reasons why Africa’s AI narratives are often missing, the implications this has for the future of AI in Africa, how the situation can be improved and the path to take to achieve responsible AI in Africa. These insights emerged following a workshop organized at Mozilla Festival 2021 and demonstrates the growing need to explore uncovered AI narratives in Africa to ensure better AI outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it