Detecting and Mitigating Indirect Stereotypes in Word Embeddings
Erin George Joyce Chew Deanna Needell
Societal biases in the usage of words, including harmful stereotypes, are
frequently learned by common word embedding methods. These biases manifest not
only between a word and an explicit marker of its stereotype, but also between
words that share related stereotypes. This latter phenomenon, sometimes called
"indirect bias,'' has resisted prior attempts at debiasing. In this paper, we
propose a novel method called Biased Indirect Relationship Modification (BIRM)
to mitigate indirect bias in distributional word embeddings by modifying biased
relationships between words before embeddings are learned. This is done by
considering how the co-occurrence probability of a given pair of words changes
in the presence of words marking an attribute of bias, and using this to
average out the effect of a bias attribute. To evaluate this method, we perform
a series of common tests and demonstrate that measures of bias in the word
embeddings are reduced in exchange for minor reduction in the semantic quality
of the embeddings. In addition, we conduct novel tests for measuring indirect
stereotypes by extending the Word Embedding Association Test (WEAT) with new
test sets for indirect binary gender stereotypes. With these tests, we
demonstrate the presence of more subtle stereotypes not addressed by previous
work. The proposed method is able to reduce the presence of some of these new
stereotypes, serving as a crucial next step towards non-stereotyped word
embeddings.