Explainable Machine Learning in Finance - When Machines Surpass Human Intelligence

Explore the critical role of explainability in AI/ML models for high-risk financial applications through this 35-minute conference talk by Nima Safaei, Sr. Data Scientist at Scotiabank. Delve into the complexities of causal and counterfactual explainability in environments with multiple exogenous factors and latent variables. Examine the importance of storytelling in extracting value from AI/ML models and effectively communicating financial metrics across organizations. Investigate the challenges of verifying machine-generated explanations by subject matter experts and consider the implications when machine intelligence surpasses human knowledge. Gain insights into these pressing questions through a causal inference perspective, addressing the balance between leveraging advanced AI capabilities and maintaining human understanding in financial decision-making processes.