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AI Model Cultural & Linguistic Alignment [Visual: network/data-node diagram — "Teaching AI to understand culture accurately"] The Challenge Large language models are often trained on data that underrepresents or misrepresents non-Western knowledge systems — including African spiritual philosophy and Yoruba cosmology. This creates models that produce inaccurate, flattened, or culturally tone-deaf responses. My Approach I developed structured Q&A datasets to help fine-tune LLMs on the Ifá epistemic corpus and Yoruba linguistics, grounding abstract philosophical concepts in accurate, well-sourced training data. I also audited existing model outputs to catch bias, factual errors, and low-resource language translation issues, then designed corrected examples and precision prompt structures for more reliable outputs. The Outcome Models trained on this work represented complex cultural material with greater accuracy and less bias.
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