That's only true in some limited cases. Section 4.1 of the paper he linked discusses extensively the limitations of linear model interpretability. Here's an ex

That's only true in some limited cases. Section 4.1 of the paper he linked discusses extensively the limitations of linear model interpretability. Here's an example: "With respect to algorithmic transparency, [the claim that linear models are more interpretable] seems uncontroversial, but given high dimensional or heavily engineered features, linear models lose simulatability or decomposability, respectively." Given how many interesting problems are high-dimensional or solved through heavy feature engineering, you must be lucky to live in the happy space where problems are either low-dimensional enough to not need heavy feature engineering or where the client is already familiar with the high-dimensional features you're going to limit the model to using.