Interpretable Models

Interpretable models are machine learning models designed to be easily understood and explained by humans, often through transparent structures or post-hoc analysis techniques. They prioritize clarity in how predictions are made, such as by using linear models, decision trees, or rule-based systems, rather than complex 'black-box' models like deep neural networks. This concept is crucial in fields where trust, fairness, and regulatory compliance are essential, such as healthcare, finance, and legal applications.

Also known as: Explainable Models, Transparent Models, White-Box Models, Interpretable AI, Explainable AI (XAI)

Why learn Interpretable Models?

Developers should learn and use interpretable models when working in domains that require accountability, such as medical diagnosis, credit scoring, or criminal justice, where stakeholders need to understand model decisions to ensure fairness and avoid bias. They are also valuable for debugging and improving model performance, as their transparency allows for easier identification of errors or biases in the data. In regulated industries, interpretable models help meet legal requirements like the EU's GDPR or the US's Fair Credit Reporting Act, which mandate explainability in automated decision-making systems.

Learning Resources

• book: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
• course: Interpretable Machine Learning with Python
• docs: Scikit-learn Documentation on Model Interpretability
• tutorial: Interpretable Machine Learning Tutorial by Kaggle
• docs: Explainable AI (XAI) Overview by Google