Why Global South countries need to care about highly capable AI
Bibliographic record
Abstract
By matching and surpassing human cognitive abilities, highly capable artificial intelligence (AI) - advanced AI systems of the foreseeable future, which leading AI companies are working toward as part of their broader goal to create artificial general intelligence - could be among the most transformative technologies the world has ever seen. While this radical technology is being built primarily in Global North countries, its impacts are likely to be felt worldwide, and disproportionately so in those Global South countries with long-standing vulnerabilities - weak-state institutions; dependence on labour-intensive, manufacturing-based and export-led economic models; regularly recurring armed conflict; high trust in technology; and more globally subordinated cultures. The authors of this paper consider six ways in which highly capable AI could interact with these vulnerabilities, and argue that unless this problem is remedied before the emergence of highly capable AI, there is a chance such AI could lead to catastrophic outcomes. Because of the significant societal impacts that highly capable AI could have, being concerned about AI in a general way will not suffice. The authors argue that all stakeholders who care about those who live in Global South countries must pull on the levers available to them with the goal of influencing the ongoing development of highly capable AI.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".