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 a new study on arXiv, global AI leaderboards fall short in their coverage of the Global South, suffering from a lack of independent governance and mechanisms for updating evaluation methods. Researchers stress that the issue is not a lack of data or benchmarks. Such tests already exist—including IndicSUPERB for India, IrokoBench for Africa, and AlGhafa for Arabic—but they are entirely absent from the 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 compels leaderboards to address deficiencies quickly when they affect paying customers in the West. In the Global South, equivalent economic leverage is missing, meaning that documented flaws in local language understanding remain uncorrected. 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 who rely on open leaderboards to select language models. Companies building solutions for multilingual markets risk employing models that perform poorly in languages such as Hindi, Swahili, or Arabic.
Impact on the EU
Swedish organisations complying with 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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