Fit interpretable models. Explain blackbox machine learning.

moDel Agnostic Language for Exploration and eXplanation (JMLR 2018; JMLR 2021)

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

[NeurIPS'24 Spotlight] A comprehensive benchmark & codebase for Image manipulation detection/localization.

Effector - a Python package for global and regional effect methods

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"

Model Agnostics breakDown plots

A Julia package for interpretable machine learning with stochastic Shapley values

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

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

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

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.

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

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

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

Data generator for Arena - interactive XAI dashboard