Considering that a significant number of words in English are gender-neutral, it is essential that debiasing methods do not adversely change their orientation.
the proposed method's ability to debias stereotypical words that carry unfair gender-biases, while preserving the genderorientation in feminine, masculine and neutral words is important when applying the debiased word embeddings in NLP applications that depend on word embeddings for representing the input texts We proposed a method to remove gender-specific biases from pre-trained word embeddings.