  It has been shown that word embeddings can exhibit gender bias, and various
methods have been proposed to quantify this. However, the extent to which the
methods are capturing social stereotypes inherited from the data has been
debated. Bias is a complex concept and there exist multiple ways to define it.
Previous work has leveraged gender word pairs to measure bias and extract
biased analogies. We show that the reliance on these gendered pairs has strong
limitations: bias measures based off of them are not robust and cannot identify
common types of real-world bias, whilst analogies utilising them are unsuitable
indicators of bias. In particular, the well-known analogy "man is to
computer-programmer as woman is to homemaker" is due to word similarity rather
than societal bias. This has important implications for work on measuring bias
in embeddings and related work debiasing embeddings.
