Course Overview

AI model risk is a new discipline, where regulatory requirements and best practices borrow heavily from other types of model risk. The practices adopted from traditional model risk management (MRM) are not always capable of dealing with the unique characteristics and challenges of AI model risk. Effective measurement, reporting and mitigation of AI model risk requires combining novel, AI-specific risk metrics and techniques with traditional risk management practices.

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 behavioural psychology of AI to describe practical and effective techniques for mitigating AI model risk and increasing reliability of AI-based workflows.

• Implement AI-based workflows based on use cases of practical
  importance to banking and asset management
• Measure aleatoric and epistemic risk of these workflows
• Mitigate the sources of both types of risk
• Measure the resulting improvements in model risk and reliability metrics

Learning Outcomes

• Understand quantitative measurement and reporting of AI model risk
• Learn the key metrics for the accuracy and reliability of AI
• Learn how to mitigate AI model risk using advanced techniques
  from behavioural psychology
• Learn to design reliable AI-based workflows

Session 1: 13.45 – 15.15
Coffee Break: 15.15 – 15.30
Session 2: 15.30 – 17.00
Q&A: 17.00 – 17.15