Study: AI leaderboards fall short in Global South coverage
A new study published on arXiv shows that global AI leaderboards lack independent governance and exclude established evaluation benchmarks from the Global South.
What happened?
According to the new study on arXiv, global AI leaderboards fail to adequately cover the Global South as they lack independent governance and mechanisms to update measurement methods. The researchers highlight that the problem is not a lack of data or evaluation benchmarks. The tests already exist—such as IndicSUPERB for India, IrokoBench for Africa, and AlGhafa for Arabic—but 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 point out that commercial pressure forces leaderboards to quickly address shortcomings when Western customers are affected. In the Global South, there is a lack of equivalent economic leverage, which means documented deficiencies in local language understanding remain unaddressed. The study uses 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 using models that perform poorly in languages such as Hindi, Swahili, or Arabic.
Impact on the EU
Swedish organisations adhering to the EU AI Act and working with multilingual compliance should note 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 review and incentives to integrate local benchmarks into global AI development.
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