TrustyAI Explainability Toolkit

Interpreting Timeseries using Local Interpretation methods

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

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

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

This project uses XAI to make AI-based Alzheimer's predictions understandable for doctors, aiming to improve diagnosis & treatment for patients

Neural network visualization toolkit for tf.keras

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

XAI for yoloV8

This repository contains the code for the XAIInferencerEngine PyPi library.

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

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

Xi method

The NLP Bias Identification Toolkit

Block code for the XAISuite library: 11301858.github.io/xaisuiteweb

CLI for XAISuite Library

ibreakdown is model agnostic predictions explainer with interactions support, library can show contribution of each feature of your prediction value

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.

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

A scoring system for explainability