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

Fit interpretable models. Explain blackbox machine learning.

Model interpretability and understanding for PyTorch

A collection of infrastructure and tools for research in neural network interpretability.

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

StellarGraph - Machine Learning on Graphs

Algorithms for explaining machine learning models

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

moDel Agnostic Language for Exploration and eXplanation

XAI - An eXplainability toolbox for machine learning

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

⬛ Python Individual Conditional Expectation Plot Toolbox

Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University

A neural network that thinks in pixels: learned colour dictionary, local cellular dynamics, self-repair, growing canvases and readable thoughts.