moDel Agnostic Language for Exploration and eXplanation

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

Algorithms for explaining machine learning models

Lime: Explaining the predictions of any machine learning classifier

Generate Diverse Counterfactual Explanations for any machine learning model.

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

Code for the TCAV ML interpretability project

Applied Machine Learning Explainability Techniques, published by Packt

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

A toolbox to iNNvestigate neural networks' predictions!

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

OpenXAI : Towards a Transparent Evaluation of Model Explanations

InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。

Interpretability and explainability of data and machine learning models

UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20 preconfigured checks (covering language, code, embedding use-cases), perform…

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

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