Popular GitHub Repositories for Interpretability

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.

Standardized Serverless ML Inference Platform on Kubernetes

Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"

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

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)

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

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

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

Interpretability and explainability of data and 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.

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

A curated list of Large Language Model (LLM) Interpretability resources.

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

XAI - An eXplainability toolbox for machine learning