Fairness in algorithmic decision-making often relies on predefined notions within a stable data landscape. However, the world we strive to model is a symphony of unprecedented change and nuanced interactions. This dynamic nature, intrinsic to evolving societies, is frequently overlooked in traditional fairness studies. We introduce a quantitative framework for diversity-aware population modeling –leveraging a Bayesian Multilevel Regression and Poststratification (MRP) strategy to mitigate unrepresentative data distributions and observed biases. Our approach integrates common and individual sources of variance in a hierarchical network, offering a unified and flexible platform to directly capture and quantify major sources of population stratification, at multiple stages of the modeling process. Our framework primarily centers on post-processing fairness techniques, reconciling existing statistical methods—specifically poststratification—with expressive generative models. We utilize the Adolescent Brain Cognitive Development (ABCD) cohort, a collaborative aggregation of longitudinal data from 11,000+ children aged 9-10, across 17 US states, for our proof of principle study. We model the effect of socioeconomic status on cognitive development, accounting for geographical and racial disparities. Poststratification provides ample stage to analyze the data distributions through a temporal lens, as confounding factors morph the real-world representations of both regional and ethnic predictors. The integration of census data, subject to annual updates, into our hierarchical model, serves a pivotal role in capturing this complexity, enabling us to finely adjust our posterior estimates by carefully recalibrating them based on precise state- and race-level proportions. We demonstrate that Bayesian MRP can be tailored to develop diversity-aware population models, providing crucial insights into dynamic fairness for generative modeling.