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

XAI - An eXplainability toolbox for machine learning

Neural network visualization toolkit for tf.keras

Principal Image Sections Mapping. Convolutional Neural Network Visualisation and Explanation Framework

Fast and incremental explanations for online machine learning models. Works best with the river framework.

The NLP Bias Identification Toolkit

SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions i.e. synergy scores.

a tool for comparing the predictions of any text classifiers

A Python package with explanation methods for extraction of feature interactions from predictive models

TrustyAI Explainability Toolkit

Official implementation of GPX: Gaussian Process Regression with Interpretable Sample-wise Feature Weights (published on TNNLS)

📺 A Python library for pruning and visualizing Keras Neural Networks' structure and weights

A library that helps to explain AI models in a really quick & easy way

Model-agnostic Statistical/Machine Learning explainability (currently Python) for tabular data

Explain Neural Networks using Layer-Wise Relevance Propagation and evaluate the explanations using Pixel-Flipping and Area Under the Curve.

A python library to agnostically explain multi-label black-box classifiers (tabular data)

Artificial Neural Networks for Java This package provides Object oriented Neural Networks for making Explainable Networks. Object Oriented Network structure is helpful for observing each and every element the model. This package is developed for XAI research and development.

XAI for yoloV8

Xi method

XAISuite: Train machine learning models, generate explanations, and compare different explanation systems with just a simple line of code.