Description: How diverse and inclusive (D&I) are we as pedagogical practitioners in the ever-changing student and actuarial science industry landscape? Given that our in-class experience impacts many populations, how successful are we dealing with conflicting belief structures and creating a welcoming and productive environment for members of the underrepresented (LGBTQIA+) and minority groups in our actuarial science classes? This interactive session will aim to provide a working knowledge of issues that arise and how to successfully address them.

Description: This article addresses how to best use body language to one's advantage

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.