Open Source AI Tools

The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.

Google's differential privacy libraries.

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

PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Algorithms for explaining machine learning models

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

An open-source framework for machine learning and other computations on decentralized data.

A Python package to assess and improve fairness of machine learning models.

Library for training machine learning models with privacy for training data