AI model risk is a new discipline, and its regulatory requirements and best practices borrow heavily from those for other types of model risk. The practices adopted from traditional model risk management are not always capable of dealing with the unique characteristics and challenges of AI model risk. Effective measurement, reporting, and mitigation of AI model require a combination of novel, AI-specific techniques and traditional model risk management techniques and practices.
AI Model Risk: Measurement, Reporting, and Mitigation
- Lecture schedule: March 24, March 31, April 7, April 14
- Delivery mode: Live online (flexible learning over four weeks, one lecture per week)
- Total lecture hours: 16 hours
- Instructor: Alexander Sokol, Executive Chairman and Head of Quant Research, CompatibL
In the first part of the course, Alexander Sokol will present practical and effective techniques for the quantitative measurement and reporting of AI model risk using both well-established and novel metrics. In the second part, Alexander will leverage his award-winning research on behavioral psychology and AI to describe practical and effective techniques for mitigating AI model risk.
The course will conclude with a hands-on workshop where participants will use what they have learned to mitigate AI model risk for several use cases of practical importance to banking and asset management. Coding will not be required for workshop participation.
Participants who successfully complete the workshop objectives will receive a certificate.
This masterclass is a joint venture, hosted by WBS Training and CompatibL.
Follow this link to learn more and register.
