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We examine whether some countries are more richly represented in embeddingspace than others. We find that countries whose names occur with low frequencyin training corpora are more likely to be tokenized into subwords, are lesssemantically distinct in embedding space, and are less likely to be correctlypredicted: e.g., Ghana (the correct answer and in-vocabulary) is not predictedfor, "The country producing the most cocoa is [MASK].". Although theseperformance discrepancies and representational harms are due to frequency, wefind that frequency is highly correlated with a country's GDP; thusperpetuating historic power and wealth inequalities. We analyze theeffectiveness of mitigation strategies; recommend that researchers reporttraining word frequencies; and recommend future work for the community todefine and design representational guarantees.

Richer Countries and Richer Representations