When does Subgroup Balance Matter? Predicting Performance Sensitivity from Latent Separability
The study investigates whether balancing training data for specific subgroups always improves performance. It found that this is not always true; performance sensitivity depends on how well subgroups are separated in the model's internal latent space. The authors propose a 'latent separation hypothesis,' which links subgroup allocation sensitivity directly to the degree of separation between groups in the pre-trained model's representations, offering a new way to guide data balancing decisions.