Characterizing, Diagnosing and Managing the Risk of Error of ML & AI Models in Clinical and Organizational Application
摘要
This chapter covers essential practical methods for examining models, reviewing their face validity, and characterizing and managing risk of errors of such models at development and at deployment stages. This chapter also briefly discusses broader methods and best practices for detecting and correcting issues with ML modeling and the emerging concept of debugging ML models and analyses. A “toolkit” for application safety measures is presented.