Joint Multiclass Debiasing of Word Embeddings
Table 1: Bias levels for gender, race, and religion before debiasing (REG) and after debiasing with Hard/SoftWEAT (HW/SW) or Conceptor (CPT)
['[EMPTY]', '[EMPTY]', 'REG', 'HW', 'SW  [ITALIC] λ=0.2', 'SW  [ITALIC] λ=0.4', 'SW  [ITALIC] λ=0.6', 'SW  [ITALIC] λ=0.8', 'SW  [ITALIC] λ=1', 'CPT']
We observe that HardWEAT removes the measured bias completely as indicated by zero WEAT scores. SoftWEAT also substantially reduces the bias measurements to different degrees for different datasets. In comparison, for example with λ=1, SoftWEAT still leads to a stronger reduction in bias compared to the state-of-the-art Conceptor algorithm in all but one measurement.