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

A library for graph deep learning research

Interpretability and explainability of data and machine learning models

moDel Agnostic Language for Exploration and eXplanation

Generate Diverse Counterfactual Explanations for any machine learning model.

XAI - An eXplainability toolbox for machine learning

👋 Xplique is a Neural Networks Explainability Toolbox

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

Neural network visualization toolkit for tf.keras

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

GraphXAI: Resource to support the development and evaluation of GNN explainers

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications

An Open-Source Library for the interpretability of time series classifiers

TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!

Fast and explainable clustering in Python

Amazon SageMaker Solution for explaining credit decisions.

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"