PAIR-code

Visualizations for machine learning datasets

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

Source code/webpage/demos for the What-If Tool

Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).

LLM Comparator is an interactive data visualization tool for evaluating and analyzing LLM responses side-by-side, developed by the PAIR team.

JavaScript implementation of UMAP

✨✍️ Wordcraft is an AI-powered text editor with an emphasis on short story writing

A tool to help researchers and product teams understand datasets with the goal of improving data quality, and mitigating fairness and bias issues.

PAIR.withgoogle.com and friend's work on interpretability methods

The Data Cards Playbook helps dataset producers and publishers adopt a people-centered approach to transparency in dataset documentation.

Lumi uses AI-powered features to help you read arXiv papers.

Understanding the theory behind UMAP

Interactive 3D / 2D webgl-accelerated scatter plot point renderer

Federated learning experiment using TensorFlow.js

Platform for running online research experiments on human + LLM group dynamics.

https://pair.withgoogle.com/explorables/