On Measuring and Mitigating Biased Inferences of Word Embeddings
Table 8: Effect of attenuating gender bias using the he-she vector, and random vectors with difference (diff) from no attenuation.
['Gender (GloVe)', 'Gender (GloVe) NN', 'Gender (GloVe) FN', 'Gender (GloVe) T:0.5', 'Gender (GloVe) T:0.7']
By removing these derived subspaces from GloVe, we demonstrate significant decrease in bias. Let us start with gender, where we removed the he-she direction, and then recomputed the various bias scores. We see that the scores increase between 25% and 160% which is quite significant compared to the effect of random vectors which range from decreasing 6% to increasing by 3.5%.