Explore the importance of model interpretability in regulated industries through a 22-minute conference talk from the Toronto Machine Learning Series. Delve into case studies illustrating sensitivity analysis for model interpretability, with a focus on healthcare and human activity applications. Learn about designing and implementing specific sensitivity tests to understand model behavior, uncover biases, and ensure fairness in risk-sensitive decision-making processes. Gain insights from ML researchers Hanieh Arjmand and Spark Tseung on balancing predictive performance with transparency and reliability in AI models, particularly in fields where model fairness is crucial.