Quantifying Error Disparities in Population Health Models

Abstract

Many high-stakes social applications of AI, such as public health surveillance and policy planning, operate at the community- rather than individual-level. However, most AI fairness research operates at the individual- or data-level (i.e. document or image) and rely on metrics defined over discrete demographic categories rather than population-level demographic proportions. In this work, we first introduce the Bilateral Concentration Index (BCI) to quantify nonmonotonic error disparities missed by the category-based metrics used at individual or data-levels. Then we conduct a large-scale audit of sociodemographic error disparities in both lexical- and transformer-based models of county-level health outcomes, over a dataset cover billions of community-mapped messages. While all tasks had significant disparity, the size varied widely depending on the outcome and model, from BCI of 2.1% for predicting life satisfaction to 17.0% for predicting fair or poor health. We further evaluate four approaches for incorporating sociodemographic information, as potential bias mitigation strategies, finding that while demographic inclusion consistently improved predictive accuracy, it frequently amplified error disparities. The largest disparities were associated with education and income (BCI = 2.7–16.4%), often reducing accuracy for low-income, and in some cases, high-income communities. These findings highlight a critical accuracy–fairness trade-off in community-level models for public health tasks, demonstrating how seemingly beneficial modeling choices can lead to increased disparities which could disadvantage communities if model predictions are used to make policy decisions in geographic areas where this public health data is not available.