AI for/by the majority world: From technologies of dispossession to technologies of radical care
Bibliographic record
Abstract
The dominant and celebratory discourse surrounding AI often fails to acknowledge the intricate dynamics and implications associated with the human, material, and environmental costs of technological development, particularly in the midst of a civilizational crisis [5]. Furthermore, hegemonic AI, primarily developed by large technology corporations, capitalizes on the resources, data, and labor of the majority world only to be deployed as a glamorous product that furthers the accumulation of privilege, wealth, and power by global elites. As a result, these hegemonic intelligent technologies originate from a predatory and violent world model that has been imposed as a universal paradigm of existence. These dominant technologies are intentionally designed to perpetuate power asymmetries. The so-called artificial intelligence, marketed as a revolutionary innovation, has proven to be the offspring of interconnected systems of oppression: a capitalist mode of production; a colonial system of epistemic, economic, social, racial, and cultural dominance; and a patriarchal order of violence that fulfills its own prophecy [10]. Artificial intelligence, driven by influential global actors with market-driven and war-driven interests, materializes as a socio-technical assemblage that optimizes capital accumulation through dispossession [3] and the exertion of violence over the territories and populations of the majority world [8]. Hegemonic AI technologies are fundamentally technologies of dispossession, appropriating the commons for their development. Their creation is governed by macro-structural forces guided by the market and powerful actors seeking control, as control is a prerequisite for wealth accumulation. Control encompasses natural resources (territory), knowledge (processing information and data), labor (productive force), bodies (labor and the capacity to produce knowledge), subjectivity (sensibility and identity), and intersubjective relations (ways of relating, living, and coexisting) [7]. Dispossession arises from the interconnections of violent systems operating at both micro and macro scales. Dispossession manifests throughout the entire lifecycle of AI, spanning from design and development to deployment, use, and disposal [6]. The human, material, and environmental costs associated with technological development are obscured by narratives emphasizing efficiency, optimization, and the automation of the world. Big capital, including finance, pharmaceuticals, agribusiness, mining, and technology, forms alliances to control global value chains and knowledge production systems, ensuring that the ultimate benefits remain concentrated in the hands of a few. Concentration of power, wealth, and knowledge widens the gaps between individuals, communities, countries, and regions, erasing them physically, socially, and epistemically. As the gap continues to widen due to the accelerating momentum of production and capitalist accumulation, the depletion of the planet’s resources and life-supporting systems draws nearer. To dismantle socio-technically mediated systems of violence, it is imperative to address power imbalances and rediscover the fundamental relational nature of existence. Alternative models of the world and dignified futures necessitate alternative models of technological development that are grounded in values associated with a radical ethics of care [1], communality [2], conviviality [4], and shared responsibility for the consequences of human impact on the planet [9].
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".