Responsible AI, SDGs, and AI Governance 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
More than ever before, AI is now an area of national strategic importance. This has become quite evident with the proliferation of national AI strategies since the first was launched in Canada in 2017. There is now an ever-growing body of national AI strategies especially in countries situated in the Global South. AI is seen as a key driver of economic development and the strategies describe how countries plan to exploit AI technologies to achieve national development goals. However, AI technologies also generate problematic and unintended consequences, and the national strategies often describe governance mechanisms for mitigating such issues. As the national development goals of many countries also align with the UN SDGs, this paper explores the relationship between responsible governance of AI, the attainment of the UN SDGs and the implications for African countries. The paper shows that there is a clear link between the development of AI and the attainment of the SDGs. Also, based on an analysis of two AI policy tracking repositories - the OECD AI Policy Observatory and Oxford AI Readiness Index – this paper shows how African countries have lagged behind countries in the Global South in terms of the development of governance structures for AI. This has far-reaching implications for the attainment of the SGDs and the paper provides recommendations in this area
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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