Digital Divide (2.0): the Shadow of AI Technology
Bibliographic record
Abstract
Abstract It is largely accepted that technology creates a digital divide in a development context. Generally, to face the digital divide, countermeasures such as capacity building, knowledge and know-how transfer, and beneficiaries’ involvement are applied to accompany technology deployment for development projects. Although the efficiency of such countermeasures is relative and debatable, they doubtlessly contribute to create an ecosystem where hope is permitted, for developing countries, to catch up and to be successful in their digital transformation process. With the advent of artificial intelligence ( AI ) and its massive worldwide promotion, such hope does not seem to be allowed anymore in developing and less-developed countries. AI technologies are designed and developed for technologically advanced environments in wealthy countries, and it has been shown that they have the potential to exacerbate problems in less-wealthy nations. In this article, it is shown that AI technology is intrinsically digital divide pro, and that there is no possible countermeasure against its potentially devastating effects on international development. This leads to a substantial concern that progress in AI technologies and the pressure to adopt them may increase inequalities both between and within countries, in ways which counteract the overall purpose of development. We call this new unbeatable form of digital divide the ‘digital divide (2.0)’, and we argue that AI is a perfect example of technologies that create and consolidate it.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".