Interpretable machine learning: Fundamental principles and 10 grand challenges

🤖 Plain-English Summary

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...

🔑 Key Findings

• 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.

🔗 Related Resources