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Model Risk Meets AI
July 17, 2025 @ 12:00 pm – 1:00 pm EDT
Event details
As AI and machine learning (ML) become increasingly integrated into high-impact decision-making, the risk governance landscape is shifting. Traditional model risk management (MRM) frameworks, designed for conventional statistical models, now face new challenges presented by AI models – challenges such as complexity, limited transparency, and rapid change.
Join Crowe specialists as we explore how foundational MRM principles – governance, testing, and life cycle oversight – can be extended and adapted to suit the needs of modern AI systems. The session will highlight key differences between traditional and AI and ML models and show how existing risk frameworks must evolve to remain relevant and effective.
We’ll examine how leading frameworks such as the National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF), ISO/IEC 42001, and state-level regulations like the Colorado AI Act are shaping expectations for AI governance. Attendees will gain a working understanding of how to navigate the intersection of compliance, transparency, and operational effectiveness across the AI model life cycle.
By attending this session, you should be able to:
- Describe how MRM principles support governance, validation, and accountability in AI model life cycles
- Distinguish between conventional and AI and ML models and evaluate how MRM must adapt to address unique risks such as complexity, data drift, and limited explainability
- Interpret emerging regulations and standards shaping AI risk oversight, including NIST AI RMF, ISO/IEC 42001, and the Colorado AI Act
- Apply a practical framework to manage AI model risk using strategies such as inventorying, risk tiering, documentation, and performance monitoring
