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.

open source interpretability platform 🧠

Explore and compare 1K+ accurate decision trees in your browser!

JavaScript library to explain any machine learning models anywhere!

AI that tries to show its work. Transparent, private, and easy to run yourself

Demos for visualizing how rule-based models work.

This repository represents the transition from behavioral safety to Neural Forensics. It provides the infrastructure to detect, audit, and mitigate high-order AI risks—such as Latent Deception, Sycophancy-Masking, and Synthetic Intimacy—directly at the mechanistic activation layer.

Model Resonance Imaging — visualize LLM internals like a brain MRI

🚀 Build interactive dashboards for explainable AI using React, TypeScript, and Vite, ensuring a smooth development experience with fast refresh and clear linting rules.

Transformer Visor: visual playback of model training

Deterministic visual proof-of-concept for the PUTMAN Model: graph activation, rigidity pruning, beam reconstruction, recursive updates, and shift metric (Δ).