Researchers Develop Framework to Manage ML Model Reliability Amid Changing Data

Scientists propose a control system that treats machine learning model reliability as a dynamic state, balancing stability against intervention costs in shifting environments. Using credit-risk data spanning 2007-2018, they show selective drift-triggered retraining reduces costs while maintaining smoother performance than continuous updates—critical for high-stakes applications.