Improving Subgroup Robustness via Data Selection

Saachi Jain, Kimia Hamidieh, Kristian Georgiev, Andrew Ilyas, Marzyeh Ghassemi, Aleksander Madry

Keywords: group robustness, fairness, data attribution, machine learning

TL;DR: Improving model performance on under-represented subpopulations by removing harmful training data.

Abstract:

Machine learning models can often fail on subgroups that are underrepresented
during training. While dataset balancing can improve performance on
underperforming groups, it requires 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.

Primary Area: Safety in machine learning

Submission Number: 11410