What-If Tool
Visually probe the behavior of trained machine learning models, with minimal coding.

overview of WIT

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

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


Tabular, Image, Text data

Ask and answer questions about models, features, and data points

What’s the latest


Contribute to the What-If Tool

The What-If Tool is open to anyone who wants to help develop and improve it!

Latest updates to the What-If Tool

New features, updates, and improvements to the What-If Tool.

Systems Paper at IEEE VAST ‘19

Read about what went into the What-If Tool in our systems papers, presented at IEEE VAST ‘19.

Playing with AI Fairness

The What-If Tool lets you try on five different types of fairness. What do they mean?
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