The Fairness of Fairness: Evaluating, Monitoring, and Impacts of ML in Insurance

Explore the technical and ethical dimensions of implementing fair machine learning models in the insurance industry through this 34-minute conference talk from the Toronto Machine Learning Series. Gain insights into calculating, interpreting, and monitoring bias metrics for assessing model fairness. Learn strategies for optimizing ML models to achieve both high fairness and accuracy. Examine the Federal Trade Commission's research on credit-based insurance scores' impact on different racial and ethnic groups. Discover valuable insights from research and current best practices in fairness analysis. Acquire a deeper understanding of the challenges and opportunities surrounding fairness in ML for the insurance sector, equipping yourself with knowledge and tools to foster transparent, ethical, and effective ML solutions in the industry. Presented by Mei Chen, Machine Learning Engineer at MunichRE, this talk offers practical strategies for achieving equitable and favorable outcomes through effective ML implementation in insurance.

The Fairness of Fairness: Evaluating, Monitoring, and Impacts of ML in Insurance