explainable-ml

In-Database Machine Learning

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Fit interpretable models. Explain blackbox machine learning.

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

A curated list of awesome machine learning interpretability resources.

🔅 Shapash makes Machine Learning models transparent and understandable by everyone

A collection of research papers and software related to explainability in graph machine learning.

A library for graph deep learning research

Interpretability and explainability of data and machine learning models

moDel Agnostic Language for Exploration and eXplanation

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Generate Diverse Counterfactual Explanations for any machine learning model.

XAI - An eXplainability toolbox for machine learning

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

OmniXAI: A Library for eXplainable AI

H2O.ai Machine Learning Interpretability Resources

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

👋 Xplique is a Neural Networks Explainability Toolbox

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu

Examples of Data Science projects and Artificial Intelligence use cases