Large-scale machine learning systems are being deployed to aid in making critical decisions in various areas of our society, including criminal justice, finance, healthcare, and education. In many cases, however, systems trained on biased data reflect or exacerbate these biases leading to unfair algorithms that disadvantage protected classes based on gender, race, sexual orientation, age, or nationality. Unfortunately, improved fairness often comes at the expense of model accuracy. Existing works addressing the fairness-accuracy tradeoff report fairness and accuracy separately at a single hyperparameter, making it impossible to compare performance between models and model families across the entire frontier. Taking inspiration from the AUC-ROC literature, we develop a method for identifying (TAF) and measuring (Fairness-AUC) the Pareto fairness-accuracy frontier. Further, we ask: Is it possible to expand the empirical Pareto frontier and thus improve the Fairness-AUC for a given collection of fitted models? We answer affirmatively by developing a novel fair model stacking framework, FairStacks, that solves a convex program to maximize the accuracy of the model ensemble subject to a relaxed bias constraint. We show that optimizing with FairStacks always expands the empirical Pareto frontier and improves the Fairness-AUC; we additionally study other theoretical properties of our proposed approach. Finally, we empirically validate TAF, Fairness-AUC, and FairStacks through studies on several real benchmark data sets, showing that FairStacks leads to major improvements in Fairness-AUC that outperform existing algorithmic fairness approaches.