Bolukbasi et al. (2016) studied from a geometrical point of view the presence of gender bias in word embeddings. For this, they compute the subspace where the gender information concentrates by computing the principal components of the difference of vector representations of male and female gender-defining word pairs. With the gender subspace, the authors identify direct and indirect biases in profession words. Finally, they mitigate the bias by nullifying the information in the gender subspace for words that should not be associated to gender, and also equalize their distance to both elements of gender-defining word pairs.