Trusted-AI/AIX360

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

A game theoretic approach to explain the output of any machine learning model.

Lime: Explaining the predictions of any machine learning classifier

Fit interpretable models. Explain blackbox machine learning.

The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.

Model interpretability and understanding for PyTorch

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

A collection of research materials on explainable AI/ML

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.