Techno-neocolonialism: an emerging risk in the artificial intelligence revolution
Bibliographic record
Abstract
Rapid advancements in artificial intelligence (AI) have the potential to be revolutionary, but they have also sparked questions about power relations and socioeconomic inequalities that are reminiscent of previous colonial practices. The risk of “techno-neocolonialism,” a phrase used to characterize the potential for dominance and exploitation analogous to historical colonial practices, is juxtaposed with the possibility of unprecedented technological advancements. This paper examines the idea of techno-neocolonialism as a modern form of dominance and exploitation, emphasizing how the AI revolution runs the risk of sustaining these practices in a globalized environment. Through an analysis of vital AI enablers like talent, data, infrastructure, and computing power, we contend that the advantages of AI are frequently centered in rich countries, marginalizing the Global South. The paper goes on to stress how important it is that cooperative frameworks give equity, respect for one another, and ethical issues top priority while developing AI. This study, which ends with a plea for fair collaborations, seeks to show the way toward a more inclusive AI ecosystem that actually empowers all parties involved while avoiding the exploitation traps that come with techno-neocolonial partnerships.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".