Conceptor Debiasing of Word Representations Evaluated on WEAT
This addresses bias in word representations for NLP applications, but it is incremental as it builds on existing debiasing methods.
The authors tackled bias in word embeddings by applying conceptor debiasing to post-process both traditional and contextualized embeddings, showing it reduces racial and gender bias as measured by the Word Embedding Association Test (WEAT).
Bias in word embeddings such as Word2Vec has been widely investigated, and many efforts made to remove such bias. We show how to use conceptors debiasing to post-process both traditional and contextualized word embeddings. Our conceptor debiasing can simultaneously remove racial and gender biases and, unlike standard debiasing methods, can make effect use of heterogeneous lists of biased words. We show that conceptor debiasing diminishes racial and gender bias of word representations as measured using the Word Embedding Association Test (WEAT) of Caliskan et al. (2017).