Interpretable AI

2 White-box models

This chapter covers

• Characteristics that make white-box models inherently transparent and interpretable
• How to interpret simple white-box models such as linear regression and decision trees
• What generalized additive models (GAMs) are and their properties that give them high predictive power and high interpretability
• How to implement and interpret GAMs
• What black-box models are and their characteristics that make them inherently opaque

To build an interpretable AI system, we must understand the different types of models that we can use to drive the AI system and techniques that we can apply to interpret them. In this chapter, I cover three key white-box models—linear regression, decision trees, and generalized additive models ...