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Language representations are known to carry stereotypical biases and, as aresult, lead to biased predictions in downstream tasks. While existing methodsare effective at mitigating biases by linear projection, such methods are tooaggressive: they not only remove bias, but also erase valuable information fromword embeddings. We develop new measures for evaluating specific informationretention that demonstrate the tradeoff between bias removal and informationretention. To address this challenge, we propose OSCaR (Orthogonal SubspaceCorrection and Rectification), a bias-mitigating method that focuses ondisentangling biased associations between concepts instead of removing conceptswholesale. Our experiments on gender biases show that OSCaR is a well-balancedapproach that ensures that semantic information is retained in the embeddingsand bias is also effectively mitigated.

OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings