OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings
Table S3: Correctly gendered Information contained by embeddings; Larger scores better as they imply more correctly gendered information expressed
['Embedding', 'WEAT*(he - she)', 'WEAT*(gendered words)', 'WEAT*(gendered names)']
Gendered names too have been seen to be good at helping capture the gender subspace [Bias1]. We represent simple projection, hard debiasing and INLP by simple projection since it is the core debiasing step used by all three. Both rows have been debiaised using the gender subspace determined using most common gendered names in Wikipedia (listed in Supplement) and correction uses in addition, We see a similar pattern in columns 2 and 3 which represent other gendered words and gendered names sans the names used to determine the subspace used for debiasing. That correction fares significantly better even among other gendered names reflects the higher degree of precision of information removal and retention.