Heterogeneity and Subgrouping in Modern Statistics and AI

Heterogeneity is a defining feature of modern data: individuals, subpopulations, sites, modalities, tasks, and time points may differ in distributions, associations, treatment effects, or decision objectives. This review develops a unified perspective in which heterogeneous analysis is a problem of structured information borrowing: deciding what should be shared, what should remain distinct, and how uncertainty about that structure should enter inference and decisions. We connect classical stratification, interaction models, finite mixtures, mixed-effects models, and empirical Bayes with subgroup discovery, individualized learning, synthetic data generation, large language models, federated learning, privacy, security, and mobile health. Precision medicine provides a central example: subgroup rules offer interpretable approximations to individualized response surfaces, whereas partially pooled models stabilize patient-specific estimation. Across applications, the same tensions recur: population averages can conceal consequential variation, unrestricted personalization is unstable, adaptive subgroup discovery creates selection bias, and borrowing across incompatible sources causes negative transfer. These difficulties are amplified in contemporary AI, where sparse user histories, entangled representations, heterogeneous preferences, feedback loops, distribution shift, and privacy constraints make reliable personalization and evaluation especially challenging. We conclude with an agenda for calibrated personalization that integrates multilevel representations, selective borrowing, subgroup-aware evaluation, and valid uncertainty quantification.