Explainable AI, also known as XAI, is a field of machine learning (ML) consisting of techniques that aim to give a better understanding of model behavior by providing explanations as to how a model made a prediction. Knowing how a model behaves, and how it is influenced by its training dataset, gives anyone who builds or uses ML powerful new abilities to improve models, build confidence in their predictions, and understand when things go awry. Explainable AI techniques are especially useful because they do not rely on a particular model—once you know an Explainable AI method, you can use it in many scenarios. This book is designed to give you the ability to understand how Explainable AI techniques work so you can build an intuition for when to use one approach over another, how to apply these techniques, and how to evaluate these explanations so you understand their benefits and limitations, as well as communicate them to your stakeholders. Explanations can be very powerful and are easily able to convey a new understanding of why a model makes a certain prediction, as Figure 1-1 demonstrates, but they also require skill and nuance to use correctly.
Figure 1-1. An explanation using Blur-IG (described in Chapter 4) that shows what pixels influenced an image classification model to predict that the animal on the cover of this book is a parrot.
Why Explainable ...
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