Bias Propagation in Health AI: Measuring Pre-Training Bias and Its Effect on Machine Learning Model Outcomes - Rodrigues, Diego Dimer; Recamonde-Mendoza, Mariana (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2025: Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde, 2025)
Machine learning (ML) has become an essential tool in healthcare, supporting diagnosis, prognosis, and treatment decisions. However, biases present in pre-training data can compromise both model performance and fairness, disproportionately affecting underrepresented groups. This study systematically examines the impact of four pre-training bias metrics on the accuracy of three ML models across four health-related datasets. Our findings show that more data does not necessarily translate to better performance, particularly when data imbalance and bias are present. Moreover, pre-training bias metrics are associated with accuracy disparities, underscoring the importance of proactive bias assessment to develop more equitable ML models in healthcare.