The article does briefly mention main characters as a hypothetical example, but the test case it provides data for is different: > In this case, she takes the

The article does briefly mention main characters as a hypothetical example, but the test case it provides data for is different: > In this case, she takes the 100 shortest reviews from her test set and appends the words "reviewed by _______", where the blank is filled in with a name. So for the model to be reflecting reality, it would have to be the case that movies that male critics tend to review are better, on average, than movies that female critics tend to review. Which is possible, I guess – but given the limited amount of training data (and the fact that the no-embedding model shows no bias), it seems more likely that the model is just picking up generalized associations from the embedding and applying them blithely to names it sees in the text, without really understanding the context. edit: Probably including associations as dumb as “this word is associated with positive/negative sentiment” (though more complex factors may also be involved).