Just as a patient wouldn't be okay with a doctor diagnosing them with cancer without giving a thorough explanation of why, neither can we fully trust a machine learning model until we understand its internal decision process.

It's imperative for a model to not only explain what decision it came to, but also why. Exploring the decision process of machine learning models, have over the last couple of years become a hot research topic, as it aims to ensure that important traits such as fairness, privacy, reliability and trust are preserved.

Join me in this session where we'll take a deep dive in to machine learning explainability, and explore a couple of techniques available for data scientists today.