Subgroup Shift

Subgroup shift, a type of data distribution change affecting specific subsets of data, poses a significant challenge to the reliability of machine learning models. Current research focuses on detecting these shifts, even when they are not apparent in overall model performance, using methods that analyze subgroup-level performance and leverage statistical hypothesis testing. This work often involves identifying and characterizing relevant subgroups, sometimes incorporating latent variables and high-dimensional data like images, to improve the robustness and interpretability of model adaptation strategies. Addressing subgroup shift is crucial for building reliable and trustworthy AI systems, particularly in high-stakes applications like healthcare, where ensuring consistent performance across diverse patient populations is paramount.