Interpretability & Explainability

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.

Easy-to-use knowledge editing framework for LLMs. Enables precise modification of model knowledge and behavior to correct hallucinations or outdated information. ACL 2024. MIT licensed.

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.

User-friendly explainability library for transparent ML models. Beautiful visualizations with explicit labels that everyone can understand. Generates web reports and integrates with SHAP/LIME. Apache 2.0 licensed.