The Fairness and Generalizability Assessment Framework

Eliane Röösli, Selen Bozkurt & Tina Hernandez-Boussard
As artificial intelligence makes continuous progress to improve quality of care for some patients by leveraging ever increasing amounts of digital health data, others are left behind. Empirical evaluation studies are required to keep biased AI models from reinforcing systemic health disparities faced by minority populations through dangerous feedback loops. We developed a broadly applicable fairness and generalizability assessment framework and used it to perform a case study on a MIMIC-trained benchmarking model. While open-science...

Registration Year

  • 2021

Resource Types

  • Software


  • Stanford University School of Medicine
  • Swiss Federal Institute of Technology in Lausanne