Fit interpretable models. Explain blackbox machine learning.

Debugging, monitoring and visualization for Python Machine Learning and Data Science

Machine Learning in one line of code

A curated list of awesome machine learning interpretability resources.

moDel Agnostic Language for Exploration and eXplanation

Interpretability and explainability of data and machine learning models

XAI - An eXplainability toolbox for machine learning

H2O.ai Machine Learning Interpretability Resources

Generate Diverse Counterfactual Explanations for any machine learning model.

📍 Interactive Studio for Explanatory Model Analysis

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

Explaining the output of machine learning models with more accurately estimated Shapley values

Fast approximate Shapley values in R

All about explainable AI, algorithmic fairness and more

(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Examples of Data Science projects and Artificial Intelligence problems

Amazon SageMaker Solution for explaining credit decisions.

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

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

Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

💡 A curated list of awesome adversarial interpretable machine learning resources

Variable importance via oscillations

Article for Special Edition of Information: Machine Learning with Python

Model Agnostic Counterfactual Explanations

Automated Transparent Genetic Feature Engineering

Rough set and machine learning data structures, algorithms and tools in Java, including algorithms for discernibility matrix, reducts, decision rules, discretization and classification, and tools: QMAK - for interactive machine learning and explainable ML, and Simple Grid Manager - for distributed experiments.