Machine Learning Model Governance

Machine learning (ML) models need governance just like other models, only more so. This is particularly true of ML models designed to improve automatically through experience. Their ability to “learn” not only enables greater accuracy and predictability, but can also greatly increase model risk and result in ethical biases. So it’s essential to establish rigorous governance processes that can quickly identify when a model begins to fail, complete with defined operating controls on inputs (data) and output (model results). The dynamic nature of ML models also means they require more frequent performance monitoring, constant data review and benchmarking, better contextual model inventory understanding, and actionable contingency plans.