Human-centered research and design to make AI partnerships productive, enjoyable, and fair.
About the team
The past few years have seen rapid advances in machine learning, with new technologies achieving dramatic improvements in technical performance. But we can go beyond optimizing objective functions. By building AI systems with users in mind from the ground up, we open up entire new areas of design and interaction.
PAIR is devoted to advancing the research and design of people-centric AI systems. We're interested in the full spectrum of human interaction with machine intelligence, from supporting engineers to understanding everyday experiences with AI.
Our goal is to do fundamental research, invent new technology, and create frameworks for design in order to drive a human-centered approach to artificial intelligence. And we want to be as open as possible: we’re building open source tools that everyone can use, hosting public events, and supporting academics in advancing the state of the art.
Our work
People + AI Guidebook
People + AI Guidebook
Model interpretibliity
Model interpretibliity
Can machines explain the "why" behind their decisions? We're investigating ways for people to understand more about ML models, starting with visualizations that look under the hood of complex systems. See this video: Visualizing High-Dimensional Space.
Tools for health
Tools for health
We are investigating how AI technology and design can help the practice of clinicians and medical researchers
Design & Machine Learning
Design & Machine Learning
How ML is changing the way we build experiences and interact with the world.
PAIR @ Medium
PAIR @ Medium
Exploring perspectives from a diverse range of contributors on participatory approaches to machine learning.
Explaining fairness
Explaining fairness
Policy tradeoffs in machine learning can be complex, but visualizations and interactive explanations can help people understand these critical issues.
Forecasting earthquake aftershock locations with AI-assisted science
Forecasting earthquake aftershock locations with AI-assisted science
Machine learning-based forecasts may one day help deploy emergency services and inform evacuation plans for areas at risk of an aftershock.
See how the world draws
See how the world draws
People who played the Quick, Draw! Game created a dataset that reflects a variety of perspectives around the globe. See visualizations of these 50 million drawings and a video about why diversity matters to machine learning.
Open-source software
What-If Tool: Analyze ML models with minimal code required
What-If Tool: Analyze ML models with minimal code required
TensorFlow.js
TensorFlow.js
TensorFlow.js is an open-source library for hardware-accelerated machine learning on the web. Train neural nets entirely in your browser, or run pre-trained models.
TCAV
TCAV
We are designing machine learning algorithms to incorporate human knowledge and be more controllable and interpretable, without sacrificing accuracy.
UMAP.js
UMAP.js
An open source, state-of-the-art dimensionality reduction algorithm, ported to JavaScript. Create interactive visualizations of high-dimensional data in the browser.
Embedding Projector
Embedding Projector
An open source tool that helps you to interactively visualize high-dimensional data. Read embeddings from your model and visualize them in two or three dimensions.
Facets
Facets
Training data can be seen as the raw ingredients for a machine learning system. A central focus of our group is helping engineers understand what goes into their models, as in the open-source Facets package.
GAN Playground
GAN Playground
Explore GAN training dynamics with this interactive visualization.
TensorFlow Playground
TensorFlow Playground
Education is central to our mission. The Playground is an open-source neural net you can play with in your browser, allowing users to experiment with different architectures through a graphical UI.
Featured publications
Embedding Projector: Interactive Visualization and Interpretation of Embeddings
Embedding Projector: Interactive Visualization and Interpretation of Embeddings
Daniel Smilkov, Nikhil Thorat, Charles Nicholson, Emily Reif, Fernanda B. Viégas, Martin Wattenberg
Direct-Manipulation Visualization of Deep Networks
Direct-Manipulation Visualization of Deep Networks
Daniel Smilkov, Shan Carter, D. Sculley, Fernanda B. Viégas, Martin Wattenberg
How to Use t-SNE Effectively
How to Use t-SNE Effectively
Martin Wattenberg, Fernanda B. Viégas, Ian Johnson
Design and Redesign in Data Visualization
Design and Redesign in Data Visualization
Fernanda B. Viégas, Martin Wattenberg
Visualizing Statistical Mix Effects and Simpson's Paradox
Proceedings of IEEE InfoVis 2014, IEEE
Ad Click Prediction: a View from the Trenches
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2013)
Interactive Visualization of Spatially Amplified GNSS Time-Series Position Fields
Interactive Visualization of Spatially Amplified GNSS Time-Series Position Fields
Brendan J. Meade, William T. Freeman, James Wilson, Fernanda B. Viégas, Martin Wattenberg
Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow
IEEE Transaction on Visualization and Computer Graphics (2017)
SmoothGrad: removing noise by adding noise
SmoothGrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, Martin Wattenberg
What Is Better Than Coulomb Failure Stress? A Ranking of Scalar Static Stress Triggering Mechanisms from 105 Mainshock-Aftershock Pairs
What Is Better Than Coulomb Failure Stress? A Ranking of Scalar Static Stress Triggering Mechanisms from 105 Mainshock-Aftershock Pairs
Brendan J. Meade, Phoebe M. R. DeVries, Jeremy Faller, Fernanda Viegas, Martin Wattenberg
Some of our people
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