Resource Allocation for Trustworthy Artificial Intelligence Projects in African Context
Bibliographic record
Abstract
Abstract This study pursues one question: What are the implications of funding disparities on the development and implementation of trustworthy AI frameworks tailored to the African context, and how can proactive strategies be employed to address these disparities towards developing the African trustworthy AI projects landscape? In response, this chapter addresses resource allocation challenges in creating a trustworthy AI framework within the African context. It highlights concerns about Western-biased AI technologies and the historical impact of colonialism on funding inadequacies, which perpetuate technological colonialism. The argument stresses the need for proactive strategies from African governments to foster AI development. Despite the projected $15.7 trillion global economic value of AI by 2030, Africa's share remains disproportionately low. For instance, in 2022, the US invested $47.7 billion in AI, while Africa's investment was only $2.0 billion. Moreover, Africa's AI investments often come from Western sources, which further exacerbates funding biases. The chapter aims to demonstrate how this funding gap hampers the development of trustworthy AI from an African perspective. Drawing on global AI projects, it advocates for addressing the funding deficit to prioritise trustworthy AI research in Africa. Furthermore, the chapter proposes an ideal trustworthy AI model aligned with African ontology, emphasising relationality and human-centeredness. Lastly, it offers insights on channelling financial resources effectively, including dormant fund utilisation, corporate social responsibility, partnerships, and community-driven initiatives, to foster a trustworthy AI framework rooted in the African ethos.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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 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".