xai-library

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

TrustyAI Explainability Toolkit

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

XAI for yoloV8

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

The NLP Bias Identification Toolkit

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

a tool for comparing the predictions of any text classifiers

Approximate Inverse Model Explanations (AIME): model-agnostic XAI through an approximate inverse operator.

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

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

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 model-agnostic library for generating explanations of machine learning predictions, supporting diverse XAI methods like CEM and LIME.

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

Quantifying the importance of features, as well as the importance of their interactions and dependencies.

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