Learn from Lydia AI ML team's workshop at the Toronto Machine Learning Scientists.
Fairness and accountability are cornerstones in regulated industries such as finance and insurance. Explainability is expected of all machine learning models in order to comply with strict audit trails and regulatory oversight. It is often challenging to bridge the gap between the realm of machine learning and regulatory needs.
This session is for machine learning scientists working in regulated industries to learn how to structure and present their work in a way that meets the needs of regulated industries. The session will describe a case study of how a machine learning team worked with an actuarial team to test models for health and life insurance underwriting and ensure sufficient explainability was achieved. The case study will offer a framework to help machine learning teams bridge the gap and get their models into production.