A key challenge in developing and deploying responsible Machine Learning (ML) systems is understanding their performance across a wide range of inputs.
Using WIT, you can test performance in hypothetical situations, analyze the importance of different data features, and visualize model behavior across multiple models and subsets of input data, and for different ML fairness metrics.
Model probing, from within any workflow
Platforms and Integrations
Compatible models
and frameworks
TF Estimators
Models served by TF serving
Cloud AI Platform Models
Models that can be wrapped in a python function
Supported data and task types
Binary classification
Multi-class classification
Regression
Tabular, Image, Text data
Ask and answer questions about models, features, and data points
What’s the latest
CODE
Contribute to the What-If Tool
The What-If Tool is open to anyone who wants to help develop and improve it!
UPDATES
Latest updates to the What-If Tool
New features, updates, and improvements to the What-If Tool.
RESEARCH
Systems Paper at IEEE VAST ‘19
Read about what went into the What-If Tool in our systems papers, presented at IEEE VAST ‘19.
ARTICLE
Playing with AI Fairness
The What-If Tool lets you try on five different types of fairness. What do they mean?
