source-assertionConfidence: highContestability: low
Large parts of the world are data-poor, and failure to reflect linguistic and cultural diversity is linked both to bias and to missed benefits.
Inspect evidence · 1 passage
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Governing AI for Humanity: Final Report (opens in a new tab)Executive summary, para. xli (p. 14)
Beyond misuse and missed use of data, the report identifies missing data: large portions of the globe are data poor. Failure to reflect linguistic and cultural diversity has been linked to bias and may also be a missed opportunity for those communities to access AI’s benefits.
Scope note: An advisory body report representing a majority consensus of its members in their personal capacity. It is not a UN member-state decision. Page locators refer to the PDF's printed page numbers.
Passage checked 2026-09-06 · un-hlab-missing-data
source-assertionConfidence: highContestability: medium
Access to compute, data and talent determines who can participate in AI; the UN advisory body proposes a floor under the AI divide rather than universal access to frontier compute.
Our reading: The 'floor' is a proposal, not an existing arrangement.
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Governing AI for Humanity: Final Report (opens in a new tab)Executive summary, section C 'Common benefits', paras. xxxviii–xl (p. 14)
Effective participation depends on access to talent, compute and data. Compute is described as one of the biggest barriers to entry; the report says it is unrealistic to promise access to compute that even the wealthiest countries and companies struggle to acquire, and instead seeks to put a floor under the AI divide.
Scope note: An advisory body report representing a majority consensus of its members in their personal capacity. It is not a UN member-state decision. Page locators refer to the PDF's printed page numbers.
Passage checked 2026-09-06 · un-hlab-compute-floor
source-assertionConfidence: highContestability: low
The African Union treats Africa’s lack of compute, and reliance on AI and data developed elsewhere, as a structural barrier and a risk to sovereignty.
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Continental Artificial Intelligence Strategy (opens in a new tab)Section 1.4.4, p. 28; Executive summary, p. 4
The strategy names the lack of high-performance compute as a main barrier for Africa, notes that all the world’s supercomputers sit in about 30 countries, and that training data usually come from developed countries.
Scope note: Global South regional body. Adopted by the AU Executive Council at its session of 18–19 July 2024 and posted by the AU on 9 August 2024; a strategy, not binding law.
Passage checked 2026-10-09 · au-compute-barrier
source-assertionConfidence: highContestability: low
India’s AI governance guidelines treat global concentration of compute, data and models as a problem to answer with subsidised access rather than a new AI-specific law.
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India AI Governance Guidelines: Enabling Safe and Trusted AI Innovation (opens in a new tab)Foreword, p. 2; section 2.1, pp. 14–15
The guidelines note that compute, data and models are concentrated among a few global players, and respond by expanding subsidised access to compute and data rather than with a new AI law.
Scope note: Non-binding guidelines; they state that a separate AI law is not needed at present.
Passage checked 2026-10-09 · india-resource-concentration
source-assertionConfidence: highContestability: low
Large language models tested on a benchmark for 17 African languages performed significantly worse than on high-resource languages such as English.
Our reading: One benchmark of 2024 models on African languages; gaps for other languages and later models may differ, but the direction matches other evaluations.
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source-assertionConfidence: mediumContestability: medium
Research with data workers in Latin America found that task instructions impose clients’ views on how data is labelled, and that precarious conditions make workers follow them rather than question them.
Our reading: Qualitative study of specific platforms and one outsourcing firm; it explains a mechanism rather than measuring its prevalence.
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The Data-Production Dispositif (opens in a new tab)Abstract
Analysing 210 instruction documents and 55 interviews, the study finds that task instructions impose clients’ worldviews on data, and that precarious work and economic dependency make workers obedient to those instructions.
Scope note: Qualitative study of data workers in Venezuela and Argentina.
Passage checked 2026-10-18 · miceli-posada-abstract