The following video is about Explainable AI (XAI) and addresses the challenge of interpreting machine learning models by making their decision-making processes transparent and understandable. It includes global explainability (how a model works overall) and local explainability (why a specific decision was made). For actuaries, XAI is valuable in validating model inputs, identifying proxy discrimination, ensuring fairness, and complying with ASOP 56 documentation standards. Tools like SHAP (Shapley Additive Explanations) break down individual predictions into feature contributions, aiding both model review and collaboration with data scientists.
Contributors: Yukki Yeung, FSA, MAAA; Sherry Chan, FSA, EA, MAAA, FCA; Jing Kai Ong, ASA; Jon Forster, ASA, MAAA