InterpretML

InterpretML is an open-source Python library developed by Microsoft Research designed to provide deep transparency into machine learning models. As of 2026, it remains a cornerstone of the Responsible AI ecosystem, bridging the gap between high-performance predictive modeling and regulatory compliance. The framework's centerpiece is the Explainable Boosting Machine (EBM), a 'glassbox' model that utilizes generalized additive models (GAMs) with interaction terms. Unlike traditional black-box models like Random Forests or XGBoost, EBMs allow users to view the exact contribution of every feature and interaction toward a final prediction without sacrificing significant accuracy. Beyond EBMs, InterpretML serves as a unified interface for popular interpretability techniques like SHAP, LIME, and Morris Sensitivity Analysis. Its technical architecture is designed for seamless integration with the scikit-learn ecosystem, making it an essential tool for sectors requiring high accountability, such as fintech, healthcare, and insurance. By providing both global and local explanations, it empowers data scientists to detect bias, debug model behavior, and build trust with non-technical stakeholders in complex production environments.