AI and vulnerabilities in sub-Saharan Africa: The need for trustworthiness, reliability and equitable access
Bibliographic record
Abstract
Sub-Sahara Africa (SSA) reserves some of the world’s oldest cultures and traditions, and with the traces of colonialism still lurking in her sociopolitical affairs, her participation in contributing significantly to achieving sustainable development goals (SDGs) and widespread adoption of artificial intelligence (AI) seem hindered by several factors including her existing conservative/traditional policies. In her quest for adopting these AI technologies, acceptable reliability measures must be put in place (as policies) to ensure that the already-existing class imbalance in SSA communities does not help in the realization of the SDGs; especially data security, and safety, and equitable access to AI technologies for SSA. Trustworthy, safe, and inclusive AI and data policies should be designed amidst the unfortunate SSA’s socio-political ecosystems to ensure equitable access. This brief unveils some vulnerabilities surrounding the use of AI in SSA and promotes equitable access to new technologies in SSA amidst the anxiety around AI and concerns about data governance.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".