Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias
Table 4: Total effects (TE) of gender bias in various GPT2 variants evaluated on the professions dataset, when separating by gender-stereotypicality.
['Model', 'Female', 'Male', 'All']
Notably, the effects are much larger in the female case. This may be explained by stereotypicaly-female professions being of higher stereotypicality than stereotypically-male professions, reflecting a societal bias viewing women’s professions as more narrowed.