To mitigate bias from word embeddings,<cite> Bolukbasi et al. (2016)</cite> propose a post-processing method to project out the bias subspace from the pre-trained embeddings. Their method is shown to reduce the gender information from the embeddings of gender-neutral words, and, remarkably, maintains the same level of performance on different downstream NLP tasks.