Machine Learning Explainability – Understanding Model Decisions
In this talk, Alex will introduce explainable AI and how to use Alibi to understand trained models.
July 21, 2022
3:00 pm GMT / 10am ET
Explainable AI, or XAI, is a rapidly expanding field of research that aims to supply methods for understanding model predictions. Alex will start by providing a general introduction to the field of explainability, introduce the Open Source Alibi library and focus on how it helps you to understand trained models. He will then explore the collection of algorithms provided by Alibi and the types of insight they each provide, looking at a broad range of datasets and models, discussing the pros and cons of each. The aim is to give the ML practitioner a clear idea of how Alibi can be used to justify, explore and enhance their use of ML, especially for models in deployment.