Community Driven AI Ethics Frameworks for Sustainable Development in Africa
Bibliographic record
Abstract
This paper develops a community driven ethical framework for artificial intelligence (AI) that aligns with sustainabledevelopment in Africa. It begins by analysing global AI ethics declarations, such as the Montréal Declaration forResponsible AI, which calls for inclusive deliberation and ecological sustainability, and the Toronto Declaration, whichcentres human rights law, equality and non discrimination. It also examines African instruments like the African Declarationon Internet Rights and Freedoms, which warns that policy processes often exclude civil society and emphasises the needfor accessible, affordable and open digital ecosystems, and Agenda 2063’s aspirations for inclusive growth, goodgovernance and a people driven future. A mixed methods approach combines normative analysis of these documents withparticipatory fieldwork in Nigerian communities and case studies of AI applications in health and agriculture. Findingsreveal a convergence on principles of human rights, fairness, inclusivity, transparency, accountability and ecologicalstewardship, while community participants stress concerns about data exploitation, algorithmic bias, privacy, equitablebenefits and preservation of cultural values. Ubuntu/Botho philosophy, which defines being human through recognizingothers’ humanity and emphasises interdependence, compassion and reciprocity, emerged as a resonant ethical lens. Theresulting framework integrates human rights based standards, African development visions and Ubuntu ethics. It proposesparticipatory governance, community data stewardship, ethical impact assessments and capacity building initiatives toensure that AI deployment in Africa supports inclusive, sustainable development while safeguarding rights and culturalvalues.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.017 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".