This video introduces the concept of AI transparency and its critical role in responsible model oversight by actuaries and risk professionals. It explains how transparency complements explainable AI by focusing not just on how a model makes decisions, but also on its development, data sources, limitations, and governance. Viewers learn about key tools like model cards, data documentation, decision traceability, and governance frameworks that enhance visibility and accountability. The session also ties transparency to actuarial standards and emphasizes its role in identifying risks like algorithmic laundering and data misuse. Practical examples and tools are provided to help actuaries ask the right questions and uphold professional responsibility in AI deployment.
Contributors: Yukki Yeung, FSA, MAAA; Sherry Chan, FSA, EA, MAAA, FCA; Jing Kai Ong, ASA; Jon Forster, ASA, MAAA