Extending the Governance Framework for Machine Learning Validation and Ongoing Monitoring

Summary

In general, an artificial intelligence (AI) and machine learning monitoring framework should be adapt to handle: model degradation, biased predictions and balance. AI governance is that strategy that then defines the policies and procedures to establish accountability for the safe development, deployment, and use of AI systems and machine learning. The established industry practices for model validation typically include the monitoring of the performance of the model, the stability of model inputs, and model calibration. By dynamically tracking a range of interconnected categories—including, but not limited to, the input data, model decay, and model robustness, risk departments can better understand potential AI and machine learning limitations and proactively identify issues that may lead to model failure. Regulatory bodies and central banks also expect risk departments to adhere to certain compliance measures when validating AI and machine learning.