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Considerations for the Interpretation of Bias Measures of Word Embeddings
Word embedding spaces are powerful tools for capturing latent semantic
relationships between terms in corpora, and have become widely popular for
building state-of-the-art natural language processing algorithms. However,
studies have shown that societal biases present in text corpora may be
incorporated into the word embedding spaces learned from them. Thus, there is
an ethical concern that human-like biases contained in the corpora and their
derived embedding spaces might be propagated, or even amplified with the usage
of the biased embedding spaces in downstream applications. In an attempt to
quantify these biases so that they may be better understood and studied,
several bias metrics have been proposed. We explore the statistical properties
of these proposed measures in the context of their cited applications as well
as their supposed utilities. We find that there are caveats to the simple
interpretation of these metrics as proposed. We find that the bias metric
proposed by Bolukbasi et al. 2016 is highly sensitive to embedding
hyper-parameter selection, and that in many cases, the variance due to the
selection of some hyper-parameters is greater than the variance in the metric
due to corpus selection, while in fewer cases the bias rankings of corpora vary
with hyper-parameter selection. In light of these observations, it may be the
case that bias estimates should not be thought to directly measure the
properties of the underlying corpus, but rather the properties of the specific
embedding spaces in question, particularly in the context of hyper-parameter
selections used to generate them. Hence, bias metrics of spaces generated with
differing hyper-parameters should be compared only with explicit consideration
of the embedding-learning algorithms particular configurations