Defining Interpretable Features. A summary of the findings and developed…

In February 2022, researchers at the Data to AI (DAI) group at MIT released a paper called "The Need for Interpretable Features: Motivation and Taxonomy" [1]. In this post, I aim to summarize some of the main points and contributions of these authors and discuss some of the potential implications and critiques of their work. I highly recommend reading the original paper if you find any of this intriguing. Additionally, if you're new to Interpretable Machine Learning, I highly recommend Christopher Molnar's free book [2]. The core finding of the paper is that even with highly interpretable models like Linear Regression, non-interpretable features can result in impossible-to-understand explanations (ex. a weight of 4 on the feature x12 means nothing to most people).