Interpretable Machine Learning
ML models that are inherently understandable – their decision logic can be directly inspected without additional explanation methods.
Interpretable ML uses inherently understandable models (Decision Trees, GAMs, EBMs) instead of black boxes – often same accuracy with full transparency.
Explanation
Examples: Decision trees, linear/logistic regression, rule lists, Generalized Additive Models (GAMs). Explainable Boosting Machines (EBMs) from InterpretML achieve near black-box accuracy with full interpretability.
Marketing Relevance
EU AI Act and GDPR prefer interpretable models for high-risk decisions. Often mandatory in regulated industries (banking, healthcare, justice).
Common Pitfalls
"Interpretable" model with 1,000 features is not truly interpretable. Decision trees can become complex through depth. Accuracy trade-off is often overestimated.
Origin & History
Cynthia Rudin argued in 2019 ("Stop Explaining Black Box Models"): Interpretable models should be preferred. InterpretML (Microsoft, 2019) delivered EBMs as a powerful alternative. Christoph Molnar's "Interpretable ML" (2020) became the standard reference.
Comparisons & Differences
Interpretable Machine Learning vs. Explainability (Post-hoc)
Interpretable ML is inherently understandable; Post-hoc explainability (SHAP, LIME) explains black boxes after the fact – can be misleading.
Interpretable Machine Learning vs. Deep Learning
Deep Learning maximizes accuracy at the cost of interpretability; Interpretable ML maximizes understandability at competitive accuracy.