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Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages

Artificial intelligence tools for education and language support are increasingly framed as scalable responses to access gaps in under-resourced communities. Yet the infrastructure underlying these tools, including training corpora, tokenization schemes, evaluation benchmarks, and deployment architectures, can systematically disadvantage speakers of underrepresented languages before a model is…

We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.

Read the original at arxiv.org →

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Sample Efficiency: Humans vs Models

Everyone agrees models need far more data than children. The size of the gap depends entirely on decisions about what to count, and those decisions move the answer by more than the disagreement they…

Schema Evolution in AI Pipelines

Upstream will add a field, rename a field, change a type from string to object, and start sending null where it never did. None of that is avoidable.

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