Regulating Artificial Intelligence through a Human Rights-Based Approach in Africa
Bibliographic record
Abstract
Abstract While the dawn of Artificial Intelligence (AI) solutions have aided in solving some of societal challenges, globalization and technological innovation potentially have the capability to disrupt, suspend, or change existing legal order, preventing the realization of business and human rights principles. For example, with AI-enabled systems, Africans can now access better healthcare, education, health, and transportation. However, AI has the potential to undermine human rights concerns. This article contextualizes the usage of AI systems and its implications for human rights violations. With particular reference to Africa, it gives an overarching context capable of constructing legal reactions to corporate related human rights violations. Some of the questions posed are: What are the ways human rights can be protected from exploitative tendencies of AI companies? How can African states, and businesses respond to regulatory challenges triggered by loss of work due to automation? What innovations and new methodologies are to be designed to engage with a sustainable and automated future? Finally, we propose reforms for corporate entities developing and deploying AI to respect human rights.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".