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Online texts -- across genres, registers, domains, and styles -- are riddledwith human stereotypes, expressed in overt or subtle ways. Word embeddings,trained on these texts, perpetuate and amplify these stereotypes, and propagatebiases to machine learning models that use word embeddings as features. In thiswork, we propose a method to debias word embeddings in multiclass settings suchas race and religion, extending the work of (Bolukbasi et al., 2016) from thebinary setting, such as binary gender. Next, we propose a novel methodology forthe evaluation of multiclass debiasing. We demonstrate that our multiclassdebiasing is robust and maintains the efficacy in standard NLP tasks.

Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings