Predictive Models for Low Birth Weight: A Comparative Analysis of Algorithmic Fairness–Improving Approaches

When developing and implementing decision-making algorithms, it is critical that model performance metrics align with management goals for the predictive tool. In our study, fairness-improving models improved accuracy and area under the curve scores for Black individuals but decreased sensitivity and negative predictive value, suggesting that the original model, although unfair, was not improved. Implementation of unfair models for allocating preventive services could perpetuate racial/ethnic inequities by failing to identify individuals most at risk for a low-birth-weight delivery.