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.

A curated list of awesome responsible machine learning resources.

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

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

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

A library for graph deep learning research

Evaluation and Tracking for LLM Experiments

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.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

XAI - An eXplainability toolbox for machine learning

OmniXAI: A Library for eXplainable AI

👋 Xplique is a Neural Networks Explainability Toolbox

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

H2O.ai Machine Learning Interpretability Resources

Examples of Data Science projects and Artificial Intelligence use-cases

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