Machine Learning for High-Risk Applications

Foreword

Renowned statistician George Box once famously stated, “All models are wrong, but some are useful.” Acknowledgment of this fact forms the foundation of effective risk management. In a world where machine learning increasingly automates important decisions about our lives, the consequences of model failures can be catastrophic. It’s critical to take deliberate steps to mitigate risk and avoid unintended harm.

Following the 2008 financial crisis, regulators and financial institutions recognized the importance of managing model risk in ensuring the safety of banks, refining the practice of model risk management (MRM). As AI and machine learning gain widespread adoption, MRM principles are being applied to manage their risk. The National Institute of Standards and Technology’s AI Risk Management Framework serves as an example of this evolution. Proper governance and control of the entire process, from senior management oversight to policy and procedures, including organizational structure and incentives, are crucial to promoting a culture of model risk management.

In Machine Learning for High-Risk Applications, Hall, Curtis, and Pandey have presented a framework for applying machine learning to high-stakes decision making. They provide compelling evidence through documented cases of model failures and emerging regulations that highlight the importance of strong governance and culture. Unfortunately, these principles are still rarely implemented outside of regulated industries, ...