NLP models trained on crowd-sourced annotations often fail to represent the perspectives of specific target populations. We introduce PAIR (Population-Aligned Instance Replication), a method that reweights training instances to align model predictions with target population perspectives. PAIR adjusts for differences between annotator pools and target populations, enabling models to better reflect the views of underrepresented groups.