Performance Is Not All You Need
Developing and Validating High Risk Machine Learning
We are experiencing unprecedented adoption of complex machine learning models across industries. However, this rapid adoption has been accompanied by a string of high-profile model failures, casting a shadow over the enthusiasm surrounding artificial intelligence. Amidst this backdrop, the battle-tested principles and lessons learned from over a decade of model risk management practices in the banking sector offer a guiding light on how to harness the power of machine learning safely.
One of the cornerstones of model risk management is model validation, a critical safeguard that extends beyond the traditional realms of software development. Unlike deterministic code, machine learning models are inherently probabilistic and prone to mistakes, necessitating a proactive approach to identify and mitigate risks before deployment.
The key components of model validation are twofold: conceptual soundness and outcome analysis. Ensuring conceptual soundness involves a rigorous examination of the data quality and suitability, variable selection, model interpretability, and benchmarking against alternative methodologies. This process is essential to validate the model's underlying assumptions and suitability for its intended use case.
Outcome analysis, on the other hand, transcends mere performance metrics. It entails a comprehensive assessment of model weaknesses, including the identification of conditions that impact output reliability, robustness against noise and corrupted data, and performance under usage drift – scenarios where the operational environment deviates from the training data.
As the frontier of AI continues to push boundaries, adhering to these time-tested principles of model validation becomes paramount. By embracing a proactive model risk management approach, we can unlock the transformative potential of AI while safeguarding against its pitfalls, paving the way for confidence deployment of safe and sound models.
About the keynote speaker
Developing and Validating High Risk Machine Learning
We are experiencing unprecedented adoption of complex machine learning models across industries. However, this rapid adoption has been accompanied by a string of high-profile model failures, casting a shadow over the enthusiasm surrounding artificial intelligence. Amidst this backdrop, the battle-tested principles and lessons learned from over a decade of model risk management practices in the banking sector offer a guiding light on how to harness the power of machine learning safely.
One of the cornerstones of model risk management is model validation, a critical safeguard that extends beyond the traditional realms of software development. Unlike deterministic code, machine learning models are inherently probabilistic and prone to mistakes, necessitating a proactive approach to identify and mitigate risks before deployment.
The key components of model validation are twofold: conceptual soundness and outcome analysis. Ensuring conceptual soundness involves a rigorous examination of the data quality and suitability, variable selection, model interpretability, and benchmarking against alternative methodologies. This process is essential to validate the model's underlying assumptions and suitability for its intended use case.
Outcome analysis, on the other hand, transcends mere performance metrics. It entails a comprehensive assessment of model weaknesses, including the identification of conditions that impact output reliability, robustness against noise and corrupted data, and performance under usage drift – scenarios where the operational environment deviates from the training data.
As the frontier of AI continues to push boundaries, adhering to these time-tested principles of model validation becomes paramount. By embracing a proactive model risk management approach, we can unlock the transformative potential of AI while safeguarding against its pitfalls, paving the way for confidence deployment of safe and sound models.