From Data Stewardship to Model Stewardship: Extending Governance Frameworks for AI Era Health Data Use

Maris et al document important ethical challenges at the intersection of electronic health record data and artificial intelligence development, but existing governance frameworks designed for secondary data use are categorically insufficient for artificial intelligence model training, which creates persistent deployable artifacts that encode local clinical patterns as generalizable knowledge. Drawing on two decades of stewardship framework development, we propose extending governance from data stewardship to model stewardship.