Taking gender-bias as a working example, we propose a debiasing method that preserves non-discriminative gender-related information, while removing stereotypical discriminative gender biases from pre-trained word embeddings.
we consider four types of information: feminine, masculine, gender-neutral and stereotypical, which represent the relationship between gender vs. bias, and propose a debiasing method that (a) preserves the genderrelated information in feminine and masculine words, (b) preserves the neutrality in genderneutral words, and (c) removes the biases from stereotypical words.