AIX360

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 Enables precise modification of model knowledge and behavior to correct hallucinations or outdated information.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.