1 BIAS IN WORD VECTORS
Table 8: WEAT positive-negative test scores before and after debiasing
['[EMPTY]', 'Before Debiasing', 'After Debiasing']
When we switch the type of bias, we also end up finding different patterns in the embeddings. In the case of both of these racial directions, there is a the split in not just occupation words but other words that are detected as highly associated with the bias subspace. It shows up foremost among the closest words of the subspace of the bias. Here, we find words like drugs and illegal close to the H-EA direction while, close to the AA-EA direction, we retrieve several slang words used to refer to African-Americans. These word associations with each racial group can be detected by the WEAT tests (lower means less bias) using positive and negative words as demonstrated by Caliskan \etal(2017) For each of these tests, we use half of the names in each category for finding the bias direction and the other half for WEAT testing. This selection is done arbitrarily and the scores are averaged over 3 such selections.