  Machine learning (ML) models are typically optimized for their accuracy on a
given dataset. However, this predictive criterion rarely captures all desirable
properties of a model, in particular how well it matches a domain expert's
understanding of a task. Underspecification refers to the existence of multiple
models that are indistinguishable in their in-domain accuracy, even though they
differ in other desirable properties such as out-of-distribution (OOD)
performance. Identifying these situations is critical for assessing the
reliability of ML models.
  We formalize the concept of underspecification and propose a method to
identify and partially address it. We train multiple models with an
independence constraint that forces them to implement different functions. They
discover predictive features that are otherwise ignored by standard empirical
risk minimization (ERM), which we then distill into a global model with
superior OOD performance. Importantly, we constrain the models to align with
the data manifold to ensure that they discover meaningful features. We
demonstrate the method on multiple datasets in computer vision (collages,
WILDS-Camelyon17, GQA) and discuss general implications of underspecification.
Most notably, in-domain performance cannot serve for OOD model selection
without additional assumptions.
