  Interpretability in machine learning (ML) is crucial for high stakes
decisions and troubleshooting. In this work, we provide fundamental principles
for interpretable ML, and dispel common misunderstandings that dilute the
importance of this crucial topic. We also identify 10 technical challenge areas
in interpretable machine learning and provide history and background on each
problem. Some of these problems are classically important, and some are recent
problems that have arisen in the last few years. These problems are: (1)
Optimizing sparse logical models such as decision trees; (2) Optimization of
scoring systems; (3) Placing constraints into generalized additive models to
encourage sparsity and better interpretability; (4) Modern case-based
reasoning, including neural networks and matching for causal inference; (5)
Complete supervised disentanglement of neural networks; (6) Complete or even
partial unsupervised disentanglement of neural networks; (7) Dimensionality
reduction for data visualization; (8) Machine learning models that can
incorporate physics and other generative or causal constraints; (9)
Characterization of the "Rashomon set" of good models; and (10) Interpretable
reinforcement learning. This survey is suitable as a starting point for
statisticians and computer scientists interested in working in interpretable
machine learning.
