  We examine whether some countries are more richly represented in embedding
space than others. We find that countries whose names occur with low frequency
in training corpora are more likely to be tokenized into subwords, are less
semantically distinct in embedding space, and are less likely to be correctly
predicted: e.g., Ghana (the correct answer and in-vocabulary) is not predicted
for, "The country producing the most cocoa is [MASK].". Although these
performance discrepancies and representational harms are due to frequency, we
find that frequency is highly correlated with a country's GDP; thus
perpetuating historic power and wealth inequalities. We analyze the
effectiveness of mitigation strategies; recommend that researchers report
training word frequencies; and recommend future work for the community to
define and design representational guarantees.

Computation and Language;Artificial Intelligence