We argue that this is due, in part, to a common feature of many interpretability methods: they analyze model behavior by using a particular dataset.

Decision trees are a fundamental workhorse in machine learning. Their logical and hierarchical structure makes them easy to understand and their predictions easy to explain.

High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained