Description: in the insurance industry, machine learning models are increasingly used to make decisions that affect people's lives. In order to ensure that models are fair, they should be audited for bias. There are no hard and fast rules for bias audit; metrics that can be used to evaluate fairness vary depending on the context in which the model is deployed. The presenters in this session present a flexible framework that highlights key considerations in bias evaluation that can be adjusted to suit your process.

Description: This article reviews the topics discussed at the December 2004 meeting of the Life and Health Actuarial task force. The covered topics include Actuarial Guideline 38 and Reserves for Variable Annuities C-3 Phase 2 reserves.

Description: Summary of a survey conducted by Milliman of 28life insurers on the impact of AG49 and AG49-A on their Indexed Universal Life products.