EasyEdit

Fit interpretable models and explain blackbox machine learning with state-of-the-art explainability techniques including Explainable Boosting Machines and SHAP-based explanations.

Gold-standard for mechanistic interpretability.

Sparse autoencoders for interpretable features.

Library for inspecting, tracing, and intervening on neural network internals at scale.

PyTorch's official interpretability library.

Comprehensive AI explainability toolkit with interpretability algorithms for data and machine learning models. Includes TED, BRCG, and ProtoNN methods for diverse explanation needs. Apache 2.0 licensed.