The article explores how some machine learning models, like linear models and decision trees, are easier to understand than others, like neural networks. The researchers aim to clarify what makes a model interpretable by focusing on these more transparent models. They find that while different models offer varying levels of interpretability, it is possible to explain what interpretability means in each case.