Interpretable Machine Learning

Later in this article we include an extensive discussion about best practices for this IML workflow to flesh out the taxonomy and deliver rigorously tested diagnostics to consumers. Ultimately, there could be an increasingly complete taxonomy that allows consumers (C) to find suitable IML methods for their use cases and helps researchers (R) to ground their technical work in real applications (as seen on the right side of Figure 2). For instance, the accompanying table highlights concrete examples of how three different potential diagnostics, each corresponding to different types of IML methods (local feature attribution, local counterfactual, and global counterfactual, respectively), may provide useful insights for three use cases. In particular, the computer vision use case from the table is expanded upon as a running example. An increasingly diverse set of methods has been recently proposed and broadly classified as part of IML. Multiple concerns have been expressed, however, in light of this rapid development, focused on IML's underlying foundations and the gap between research and practice.