  Existing bias mitigation methods to reduce disparities in model outcomes
across cohorts have focused on data augmentation, debiasing model embeddings,
or adding fairness-based optimization objectives during training. Separately,
certified word substitution robustness methods have been developed to decrease
the impact of spurious features and synonym substitutions on model predictions.
While their end goals are different, they both aim to encourage models to make
the same prediction for certain changes in the input. In this paper, we
investigate the utility of certified word substitution robustness methods to
improve equality of odds and equality of opportunity on multiple text
classification tasks. We observe that certified robustness methods improve
fairness, and using both robustness and bias mitigation methods in training
results in an improvement in both fronts
