Artificial intelligence for development (AI4D): A contested notion
Bibliographic record
Abstract
Recently, the notion of artificial intelligence for development (AI4D) has been mobilized by various actors in the global South and North. We identify five analytical categories to help us understand the different and often contested perspectives on AI4D. They are (a) a developmentalist framework that emphasizes discourses around modernity and progress through a technoliberal lens of ‘catching up’; (b) an economic development framework taken up by African states, private sector and civil society, highlighting a positive and more future-looking outlook on AI's potential for development; (c) an international policy framework tied to globally agreed on policies such as the Sustainable Development Goals; (d) a colonial and extractivist framework that articulates how AI4D reinforces old processes of oppression in new ways; and (e) decolonial AI discourses grounded in Latin American, African and Indigenous approaches. Our critical review of literature on AI4D and related expressions shows that while the notion applies broadly to the global South, the majority of publications use the term in reference to AI development on the African continent. This commentary enriches our understanding of the plurality of meanings, where they come from, what they do, and what they leave unaddressed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.082 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".