Review
Open access
Jul 2026
A framework for assessing algorithmic discrimination risks in training data: a case of pediatric type 1 diabetes
A generalizable framework for identifying algorithmic discrimination risks arising from subgroup imbalances in machine learning training data, with relevance to medical informatics applications where heterogeneous real-world data can bias model behavior is introduced.
I. Bilionis, Ricardo C. Berrios, A. de Arriba Muñoz et al.
· JAMIA Open · 0 citations