Responsible AI for Public Health: A Methodological Illustration with a Forecasting Model applied to Respiratory Hospitalizations on SUS Data - Pereira, Ramon G.; Brito, Luís Eduardo Limas; Avelar, Italo; Carvalho, Matheus; Vasconcelos, Marisa; Brandão, Michele A.; Meira Jr, Wagner (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2026)
This paper investigates how Responsible AI principles can be systematically integrated into public health predictive modeling using data from the Brazilian Unified Health System. We operationalize a four-layer architecture embedding governance, leakage-controlled temporal validation, fairness auditing, explainability, and structured documentation into the modeling lifecycle. In predicting monthly respiratory hospitalizations, LightGBM achieved an RMSE of 13.81 but exhibited a 44.8 percent sMAPE disparity in the highest disparity state. A resampling adjustment reduced this gap by 8.51 percentage points. The results demonstrate how structured Responsible AI controls reshape evaluation beyond aggregate predictive accuracy.