explainable-artificial-intelligence

moDel Agnostic Language for Exploration and eXplanation

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

📍 Interactive Studio for Explanatory Model Analysis

A generalized gradient-based CNN visualization technique

A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI )

All about explainable AI, algorithmic fairness and more

Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification

Visualizing how deep networks make decisions

Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

Model verification, validation, and error analysis

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.

This project provides GOLang implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization aimed to solve deceptive tasks with strong local optima

A CUDA implementation of the Tsetlin Machine based on bitwise operators

SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments

Interactive XAI dashboard

Variable importance via oscillations

Automated Transparent Genetic Feature Engineering

Explainable AI for Software Engineering: A Hands-on Guide on How to Make Software Analytics More Practical, Explainable, and Actionable (https://xai4se.github.io)

Concept activation vectors for Keras

Case Study course for DS studies in Summer 2020/2021

Code and data from the paper "Targeted Nonlinear Adversarial Perturbations in Images and Videos".

The implementation of evolvable-substrate HyperNEAT algorithm in GO language. ES-HyperNEAT is an extension of the original HyperNEAT method for evolving large-scale artificial neural networks.

EUCA is a practical prototyping tool to design explainable AI for non-technical end-users

Explanations in Multi-Model Planning

Minimize the risks and maximize the benefits of using data-driven technologies within government processes, programs and services through transparency. | Réduire les risques et à maximiser les avantages liés à l’utilisation de technologies axées sur les données, dans le cadre de processus, programmes et services gouvernementaux, grâce à la transparence.

ShapleyVIC: Shapley Variable Importance Cloud for Interpretable Machine Learning

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