In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Different distribution shifts require different algorithmic and operational interventions.
Methodological research must be grounded by the specific shifts they address. Although …

A major challenge to out-of-distribution generalization is reliance on spurious features—
patterns that are predictive of the class label in the training data distribution, but not causally …

Despite increasing numbers of regulatory approvals, deep learning-based computational
pathology systems often overlook the impact of demographic factors on performance …

While deep learning models have shown remarkable performance in various tasks, they are
susceptible to learning non-generalizable _spurious features_ rather than the core features …

Machine learning models, while progressively advanced, rely heavily on the IID assumption,
which is often unfulfilled in practice due to inevitable distribution shifts. This renders them …

Machine learning models often fail to generalize well under distributional shifts.
Understanding and overcoming these failures have led to a research field of Out-of …

Abstract Machine learning models often perform poorly under subpopulation shifts in the
data distribution. Developing methods that allow machine learning models to better …

Machine learning algorithms learned from data with skewed distributions usually suffer from
poor generalization, especially when minority classes matter as much as, or even more than …