Interpretable machine learning: Fundamental principles and 10 grand challenges

Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. 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...

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• 1 In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic.
• 2 We also identify 10 technical challenge areas in interpretable machine learning and provide history and background on each problem.
• 3 Some of these problems are classically important, and some are recent problems that have arisen in the last few years.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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