An integrated approach to gender equality, diversity, and inclusion in the development of artificial intelligence tools in agriculture and food system 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
Abstract Agriculture in sub-Saharan Africa faces complex challenges, such as low productivity, climate stress, and ongoing social inequalities, particularly affecting women and marginalised groups. Whilst artificial intelligence (AI) holds transformative potential for agriculture and food systems, its development often overlooks these stakeholders, thereby reinforcing existing disparities. This study investigates two AI research initiatives in Nigeria and Uganda that employed a design-by-inclusion approach rooted in gender equality, diversity, and inclusion (GEDI) principles. Through retrospective case studies involving small groups of women and persons with disabilities, we examine how participatory engagement influenced the relevance, usability, and confidence of AI tools amongst users. Drawing on insights from Feminist Human–Computer Interaction (HCI) and Design Justice, our analysis demonstrates that inclusive processes led to significant improvements in participants’ confidence and willingness to engage with AI tools. Based on these findings, we propose a practical framework for developing inclusive AI in agriculture. This work underscores the importance of context-sensitive, participatory design in fostering equitable and effective AI innovations within African agriculture.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.013 |
| 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 it