This course explores explainable artificial intelligence (XAI), covering essential background definitions and concepts, explainable feature engineering, the diverse ecosystem of explainable models, post-hoc explanation methods, and some of the latest developments in audit and transparency laws and regulations.

Attendees will learn to build accurate, transparent, and understandable ML models that align to transparency regulations and supervisory guidance in consumer finance. They will also learn how to interpret and explain sophisticated ML algorithms, enabling them to uncover insights, identify biases, and make data-driven decisions in complex scenarios. Moreover, attendees will be equipped to build safer and more trustworthy AI systems, tackle real-world problems confidently, and drive positive impact in various domains such as credit underwriting and resources.