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

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

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

Fit interpretable models. Explain blackbox machine learning.

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

Model interpretability and understanding for PyTorch

A curated list of awesome responsible machine learning resources.

StellarGraph - Machine Learning on Graphs

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

Algorithms for explaining machine learning models

FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

[ICCV 2017] Torch code for Grad-CAM

A JAX research toolkit for building, editing, and visualizing neural networks.

moDel Agnostic Language for Exploration and eXplanation

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

A collection of research materials on explainable AI/ML

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

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.

XAI - An eXplainability toolbox for machine learning