An AI development team trains a large language model to assist with writing professional emails. After deployment, they receive feedback that the model's suggestions for users with non-Western names often sound overly casual or grammatically awkward, while suggestions for users with common Western names are consistently high-quality. The training data consisted primarily of a large, publicly available email corpus from a North American tech company. What is the most likely reason for this performance discrepancy, and which action would be the most effective first step to address it?

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