Study: AI leaderboards lack coverage of the Global South
A new study published on arXiv reveals that global AI leaderboards lack independent governance and exclude established evaluation benchmarks from the Global South.
What happened?
According to the new arXiv study, global AI leaderboards fail to adequately cover the Global South as they lack independent governance and mechanisms for updating evaluation methods. Researchers highlight that the issue is not a lack of data or benchmarks. These tests already exist—such as IndicSUPERB for India, IrokoBench for Africa, and AlGhafa for Arabic—yet they are entirely absent from leading commercial and academic leaderboards.
Key facts
| Studietyp | Positionsartikel (arXiv cs.AI) |
|---|---|
| Fallstudie | Indien (1,4 miljarder invånare, 22 officiella språk) |
| Regionala benchmark-tester | IndicSUPERB, IrokoBench, AlGhafa |
”This position paper argues that AI leaderboards are structurally ill-suited to serving the Global South because they lack independent governance, conflict-of-interest policies, and mechanisms for metric evolution.”
Why it matters
Researchers suggest that commercial pressure forces leaderboards to quickly address flaws when they affect paying customers in the West. In the Global South, there is a lack of corresponding economic leverage, meaning documented deficiencies in local language understanding remain unaddressed. The study cites India, with its 1.4 billion inhabitants and 22 official languages, as an example of a market where high-quality evaluation data exists but lacks an independent aggregator.
Who is affected?
The report concerns AI developers, researchers, and authorities that rely on open leaderboards to select language models. Companies building solutions for multilingual markets risk deploying models that perform poorly on languages such as Hindi, Swahili, or Arabic.
Impact on the EU
Swedish organisations adhering to the EU AI Act and working on multilingual compliance should take note of the researchers' criticism regarding the lack of validation for regional language models on global evaluation platforms.
What else you should know
The work is based on a position paper published on arXiv (cs.AI), in which the authors highlight the need for independent oversight and incentives to integrate local benchmarks into global AI development.
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