Fit interpretable models. Explain blackbox machine learning.

moDel Agnostic Language for Exploration and eXplanation

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

H2O.ai Machine Learning Interpretability Resources

📍 Interactive Studio for Explanatory Model Analysis

💡 Adversarial attacks on explanations and how to defend them

Model Agnostics breakDown plots

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"

A Julia package for interpretable machine learning with stochastic Shapley values

Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

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

An interactive framework to visualize and analyze your AutoML process in real-time.

Unofficial implementation of MVSS-Net (ICCV 2021) with Pytorch including training code.

Effector - a Python package for global and regional effect methods

Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN

Data generator for Arena - interactive XAI dashboard

Surrogate Assisted Feature Extraction in R

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/