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Bias in word embeddings such as Word2Vec has been widely investigated, andmany efforts made to remove such bias. We show how to use conceptors debiasingto post-process both traditional and contextualized word embeddings. Ourconceptor debiasing can simultaneously remove racial and gender biases and,unlike standard debiasing methods, can make effect use of heterogeneous listsof biased words. We show that conceptor debiasing diminishes racial and genderbias of word representations as measured using the Word Embedding AssociationTest (WEAT) of Caliskan et al. (2017).

Conceptor Debiasing of Word Representations Evaluated on WEAT