White Box Models

White box models, also known as interpretable or transparent models, are machine learning or mathematical models where the internal logic, parameters, and decision-making processes are fully understandable and explainable to humans. They contrast with black box models, which operate opaquely, and are commonly used in fields like linear regression, decision trees, or rule-based systems. This transparency allows stakeholders to audit, validate, and trust the model's predictions by examining how inputs lead to outputs.

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

Why learn White Box Models?

Developers should learn and use white box models in scenarios where interpretability, regulatory compliance, or ethical considerations are critical, such as in healthcare diagnostics, financial lending, or legal applications where decisions must be justified. They are essential for debugging models, ensuring fairness, and building trust with end-users, as they provide clear insights into feature importance and decision pathways, reducing risks of bias or errors.

Learning Resources

• book: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
• docs: Scikit-learn Documentation on Model Interpretability
• course: Coursera Course: Interpretable Machine Learning
• video: YouTube Video: White Box vs Black Box Models Explained
• tutorial: Towards Data Science Article on White Box Models