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. In this session, we present a flexible framework that highlights key considerations in bias evaluation that can be adjusted to suit your process. In this session, we will learn about different metrics to assess fairness, discuss open source tools that can be used for bias audit, and walk through a case study.

Description: A flipped classroom moves the traditional lecture component of teaching outside of normal classroom time, so that lectures can be replaced with active learning activities during class time. In this talk, I first present a brief overview of flipped classrooms, with strategies for implementation and resources provided. Second, I will discuss the details of its application replacing two lectures in my third-year Life Contingencies course. Students’ responses to the technique are also provided in the form of data from anonymous post-activity surveys.