Are certain African ethical values at risk from artificial intelligence?
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 This paper questions how the drive toward introducing artificial intelligence (AI) in all facets of life might endanger certain African ethical values. It argues in the affirmative that indeed two primary values that are prized in nearly all versions of sub-Saharan African ethics (available in the literature) might sit in direct opposition to the fundamental motivation of corporate adoption of AI; these values are Afro-communitarianism grounded on relationality, and human dignity grounded on a normative conception of personhood. This paper offers a unique perspective on AI ethics from the African place, as there is little to no material in the literature that discusses the implications of AI on African ethical values. The paper is divided into two broad sections that are focused on (i) describing the values at risk from AI and (ii) showing how the current use of AI undermines these said values. In conclusion, I suggest how to prioritize these values in working toward the establishment of an African AI ethics framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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