Figure 1 illustrates the results of <cite>(Bolukbasi et al. 2016 )</cite> bias detection algorithm for a list of profession names with word embedding vectors of dimension 256 trained using the Skipgram algorithm (Mikolov et al. 2013 ) on a sample of 23k Wikipedia articles with 50k term vocabulary. As one can observe, this bias measure identifies some profession names such as commander and nurse, which historically were predominantly male and female jobs, respectively. Moreover, we trained word embeddings with differing dimension on the same sampled Wikipedia corpus using Skip-gram algorithm, and calculated Kendall tau rank correlation coefficients for the bias metrics corresponding to terms in the above list of professions. As illustrated in Figure 2 , we found that though rankings for low dimensional embeddings were unstable, for larger dimensions (≥ 128) rankings of the biases of the profession terms achieve superior Kendall tau scores.