AI Model Risk Management Framework
Released: 07/23/2024
Sophisticated machine learning (ML) models present exciting opportunities in fields such as predictive maintenance and smart supply chain management. While these ML models hold the potential to unlock significant innovation, their increasing use also introduces inherent risks. Unaddressed model risks can lead to substantial financial losses, regulatory issues, and reputational harm. To address these concerns, we need a proactive approach to risk management.
This paper from the CSA AI Technology and Risk Working Group discusses the importance of AI model risk management (MRM). It showcases how model risk management contributes to responsible AI development and deployment and explores the core components of the framework. These components work together to identify and mitigate risks and improve model development through a continuous feedback loop.
Key Takeaways:
- Benefits of a comprehensive AI risk management framework, including the more responsible use of AI, enhanced transparency, informed decision-making processes, and robust model validation
- Elements, benefits, and limitations of the four core components: AI model cards, data sheets, risk cards, and scenario planning
- How to combine the core components into a comprehensive AI risk management framework
Best For:
- AI/ML Engineers and Developers
- Data Scientists
- Risk Management Professionals
- Compliance Officers and Auditors
- Business Leaders, Executives, and Project Managers
- Communications and Public Relations Professionals
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