On the missing data layer and a potential solution
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Abstract (arXiv)
Latin America is missing two foundational layers of AI infrastructure: the dataset layer and the benchmark layer. This paper targets the dataset layer. The dataset layer faces two compounding problems: discovery and supply. Latin American AI datasets exist but are scattered across platforms with no shared index. Even with perfect indexing, the total volume would remain far below what frontier AI development requires. We propose DataHub: a task-first data infrastructure organized through the ontology /<task?>/<domain?>/<language?>, with mechanisms for dataset discovery, metadata, contribution, licensing, and reuse.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 3 Aug 2026.
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References · 7
- 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
- 2018Datasheets for Datasetscited by 1,386
- 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 260
- 2023Cultural Bias and Cultural Alignment of Large Language Modelscited by 149
- 2024Large Language Models are Geographically Biasedcited by 93
- 2024Croissant: A Metadata Format for ML-Ready Datasetscited by 81
- 2022Modeling community standards for metadata as templates makes data FAIRcited by 5
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