  Machine learning models can fail on subgroups that are underrepresented
during training. While techniques such as dataset balancing can improve
performance on underperforming groups, they require access to training group
annotations and can end up removing large portions of the dataset. In this
paper, we introduce Data Debiasing with Datamodels (D3M), a debiasing approach
which isolates and removes specific training examples that drive the model's
failures on minority groups. Our approach enables us to efficiently train
debiased classifiers while removing only a small number of examples, and does
not require training group annotations or additional hyperparameter tuning.
