Addressing Both Statistical and Causal Gender Fairness in NLP Models
Hannah Chen Yangfeng Ji David Evans
Statistical fairness stipulates equivalent outcomes for every protected
group, whereas causal fairness prescribes that a model makes the same
prediction for an individual regardless of their protected characteristics.
Counterfactual data augmentation (CDA) is effective for reducing bias in NLP
models, yet models trained with CDA are often evaluated only on metrics that
are closely tied to the causal fairness notion; similarly, sampling-based
methods designed to promote statistical fairness are rarely evaluated for
causal fairness. In this work, we evaluate both statistical and causal
debiasing methods for gender bias in NLP models, and find that while such
methods are effective at reducing bias as measured by the targeted metric, they
do not necessarily improve results on other bias metrics. We demonstrate that
combinations of statistical and causal debiasing techniques are able to reduce
bias measured through both types of metrics.