  Data-driven predictive solutions predominant in commercial applications tend
to suffer from biases and stereotypes, which raises equity concerns. Prediction
models may discover, use, or amplify spurious correlations based on gender or
other protected personal characteristics, thus discriminating against
marginalized groups. Mitigating gender bias has become an important research
focus in natural language processing (NLP) and is an area where annotated
corpora are available. Data augmentation reduces gender bias by adding
counterfactual examples to the training dataset. In this work, we show that
some of the examples in the augmented dataset can be not important or even
harmful for fairness. We hence propose a general method for pruning both the
factual and counterfactual examples to maximize the model's fairness as
measured by the demographic parity, equality of opportunity, and equality of
odds. The fairness achieved by our method surpasses that of data augmentation
on three text classification datasets, using no more than half of the examples
in the augmented dataset. Our experiments are conducted using models of varying
sizes and pre-training settings.
