The white-box model approach aims for interpretable AI

When building machine learning models or algorithms, developers should adhere to the principle of interpretability so that they and their intended users know exactly how the inputs and inner workings achieve outputs. Interpretable AI is a book written by Ajay Thampi, a machine learning engineer at Meta, and its second chapter explains the white-box model approach to machine learning as well as examples of white-box models. These models are interpretable, as they feature easy-to-understand algorithms that show how data inputs achieve outputs or target variables. Thampi walks readers through three types of white-box models in this chapter and how they are applied: linear regression, generalized additive models (GAMs) and decision trees. Given the term regression in machine learning refers to models and algorithms taking data and learning relationships within that data to make predictions, the premise of a linear regression model is that a target prediction variable can be determined as a linear combination of every input variable.