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

Evaluation and Tracking for LLM Experiments

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

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

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.

moDel Agnostic Language for Exploration and eXplanation

Generate Diverse Counterfactual Explanations for any machine learning model.

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