/

Google What-If Tool

  • What it is:Google What-If Tool is a visual interface that enables users to explore, analyze, and debug machine learning models by testing scenarios, examining fairness across subgroups, and generating counterfactual examples.
  • Best for:ML researchers analyzing model behavior, TensorFlow practitioners, AI fairness researchers
  • Pricing:Free tier available, paid plans from varies
  • Rating:95/100Excellent
  • Expert's conclusion:(71) A necessary free tool for all ML practitioners who need to investigate how well their models perform and behave, and which can help them debug model predictions during the development stage.
Visit website
Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Google What-If Tool and What Does It Do?

Google is a global tech firm that specializes in Internet-based services and products, as well as cloud computing, and artificial intelligence (AI) research. The What-If Tool was created by the PAIR (People + AI Research) team at Google along with the Google Research team. The tool aims to make the use of AI technologies available to machine learning professionals around the world.

Active
📍Mountain View, CA
📅Founded 1998
🏢Public
TARGET SEGMENTS
DevelopersML EngineersData ScientistsResearchers

What Are Google What-If Tool's Key Business Metrics?

📊
TensorFlow, custom models
Models Supported
📊
Jupyter, TensorBoard, Google Cloud AI Platform
Integrations
📊
Yes (GitHub)
Open Source

How Credible and Trustworthy Is Google What-If Tool?

95/100
Excellent

Created by Google Research which has also documented it and integrated it into many of the most widely used machine learning platforms, therefore it can be considered to have achieved a very high level of maturity and trustworthiness.

BREAKDOWN
Product Maturity95/100
Company Stability100/100
Security & Compliance95/100
User Reviews90/100
Transparency95/100
Support Quality90/100
TRUST SIGNALS
Developed by Google ResearchOpen source on GitHubPublished in peer-reviewed researchIntegrated with Google Cloud AI PlatformUsed by ML practitioners worldwide

What is the history of Google What-If Tool and its key milestones?

1998

Google Founded

Google was established by co-founders Larry Page and Sergey Brin while they were students at Stanford University in California.

2015

Alphabet Restructured

Google became a subsidiary of Alphabet Inc.

2018

What-If Tool Released

Published a research paper by the Google PAIR team who launched this tool.

2019

Cloud AI Platform Integration

This tool was integrated into Google Cloud AI Platform for use in production models.

Who Are the Key Executives Behind Google What-If Tool?

Sundar Pichai— CEO, Google and Alphabet
Andy Rubin leads Google's AI efforts including those of Google Research and DeepMind. Prior to leading Google's AI efforts he was Product Chief responsible for Chrome and Android.
Jeff Dean— Chief Scientist, Google
Andy Rubin leads Google's AI effort and is also the co-founder of TensorFlow and a prominent figure in ML infrastructure.

What Are the Key Features of Google What-If Tool?

✨
Interactive Model Analysis
Users can probe the same model but input the model differently to determine whether there are differences in its ability to perform robustly or whether it is biased when making predictions about certain groups of people using an interactive widget.
✨
Fairness Visualization
Users can visualize how their model performs on different demographic slices and under fairness constraints.
✨
Partial Dependence Plots
Users can see how changing one of the individual features of each record will affect the model's predictions for each specific record.
✨
Counterfactual Examples
Users can see the closest records to the current record that are being predicted to have a different prediction outcome than the current record to help them understand what the decision boundaries of their model look like.
✨
Performance + Fairness Tab
Users can aggregate performance metrics on their model for different slices of their dataset to help identify where their model may be exhibiting biases based on the features of the records in their dataset.
✨
Dataset Slicing
Users can compare how well their model is performing on different subsets of their data to help them identify the areas of weakness for their model.
📊
Multi-Platform Support
The What If Tool can be used within Jupyter Notebooks, TensorBoard, and Google Cloud AI Platform.

What Technology Stack and Infrastructure Does Google What-If Tool Use?

Infrastructure

Google Cloud Platform

Technologies

TensorFlowJavaScriptD3.jsPolymer

Integrations

Jupyter NotebooksTensorBoardGoogle Cloud AI Platform

AI/ML Capabilities

Model-agnostic analysis tool supporting TensorFlow and other ML frameworks for interactive visualization and fairness evaluation

Based on official GitHub repository, research paper, and Google Cloud documentation

What Are the Best Use Cases for Google What-If Tool?

Machine Learning Engineers
Before deploying the model users can test how robust the model will be to testing it using counterfactual examples and partial dependence plots using the widget interface of the What If Tool.
AI Fairness Researchers
Users can visualize how their model is fair and how disparate performance occurs across different demographic slices by viewing how the fairness constraints of their model impact its performance, and identifying where algorithmic bias may exist.
Data Scientists
Users can interactively learn about the importance of the features in their model and how their model behaves for specific records without having to retrain the model using the widget interface of the What If Tool.
Production ML Teams
Monitor your deployed machine learning (ML) models on Google Cloud AI Platform for performance degradation and fairness drift.
NOT FORNon-Technical Stakeholders
Not suitable - it requires you to have a working knowledge of ML and Python/Jupyter to set up and interpret visualizations.
NOT FORReal-time Inference Systems
Only applicable to a certain extent - it's designed as an exploratory tool and not as a way to optimize the production of your ML models for inference.

How Much Does Google What-If Tool Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Google What-If ToolFreeOpen-source tool, no subscription or usage fees requiredOfficial website
Google What-If ToolFree
Open-source tool, no subscription or usage fees required
Official website

How Does Google What-If Tool Compare to Competitors?

FeatureGoogle What-If ToolFacetLit.aiTrueFoundry
Core functionalityModel debugging & bias analysisModel debugging & performanceLLMOps monitoringMLOps platform
Pricing (starting price)FreeFree tier$20/moCustom
Free tier availabilityYes (fully open-source)YesNoNo
Enterprise features (SSO, audit logs)—YesYesYes
API availabilityYes (TensorFlow integration)YesYesYes
Integration countTensorBoard, ColabMultiple ML frameworksLLM-focusedKubernetes, MLflow
Support optionsCommunity/GitHubEmail/SlackPriorityEnterprise support
Security certifications—SOC 2SOC 2ISO 27001
Core functionality
Google What-If ToolModel debugging & bias analysis
FacetModel debugging & performance
Lit.aiLLMOps monitoring
TrueFoundryMLOps platform
Pricing (starting price)
Google What-If ToolFree
FacetFree tier
Lit.ai$20/mo
TrueFoundryCustom
Free tier availability
Google What-If ToolYes (fully open-source)
FacetYes
Lit.aiNo
TrueFoundryNo
Enterprise features (SSO, audit logs)
Google What-If Tool—
FacetYes
Lit.aiYes
TrueFoundryYes
API availability
Google What-If ToolYes (TensorFlow integration)
FacetYes
Lit.aiYes
TrueFoundryYes
Integration count
Google What-If ToolTensorBoard, Colab
FacetMultiple ML frameworks
Lit.aiLLM-focused
TrueFoundryKubernetes, MLflow
Support options
Google What-If ToolCommunity/GitHub
FacetEmail/Slack
Lit.aiPriority
TrueFoundryEnterprise support
Security certifications
Google What-If Tool—
FacetSOC 2
Lit.aiSOC 2
TrueFoundryISO 27001

How Does Google What-If Tool Compare to Competitors?

vs Facet

The Google What-If Tool is a totally free, open-source product that focuses specifically on the interpretability and bias detection of ML models, while Facet is a much broader tool for observing the behavior of your ML models with commercial versions available. What-If is superior at integrating into the TensorFlow ecosystem, however, it does not provide the same level of enterprise support as Facet.

Choose What-If Tool if you need to perform what-if analysis on your ML models using a free, research-focused tool; choose Facet if you need to monitor the performance of your ML models in a production environment.

vs Lit.ai

Lit.ai focuses on bias and safety testing specific to Large Language Models (LLMs), while the What-If Tool is a general purpose ML model what-if analysis tool that is free. Lit.ai is more focused on LLMs than Lit, while What-If provides more direct integration into Google Cloud.

Use What-If Tool when you want to do research on your ML model using TensorFlow/ML; use Lit.ai when you want to ensure that the Large Language Models that you deploy will operate safely.

vs TrueFoundry

While both tools can be used for model interpretability and bias detection, they are very different products. What-If Tool is a specialized tool for doing what-if analysis, while TrueFoundry is a full MLOps platform providing deployment features. While TrueFoundry supports all of an organization's enterprise needs, What-If Tool supports the needs of researchers and organizations performing early experimentation.

Use What-If Tool to debug your ML model; use TrueFoundry to manage the entire ML development lifecycle from build to deployment.

What are the strengths and limitations of Google What-If Tool?

Pros

  • Totally free - it is open source and there are no usage limits or licensing fees associated with using it.
  • Provides deep insights into how your ML model makes its predictions - allows you to visualize predictions across the feature space.
  • Allows you to detect bias in your ML model - analyzes fairness across multiple data slices.
  • Seamlessly integrates with Google Colab - you can run What-If Tool directly in your Google Colab notebooks.
  • Compatible with TensorBoard - you're already familiar with the interface if you work with ML.
  • Actively maintained by Google - it is part of their PAIR research initiative.
  • Provides no vendor lock-in - it is fully self-hostable and can be extended.

Cons

  • Is primarily a TensorFlow-centric product - there is limited support for other ML frameworks.
  • Has a steep learning curve - you need to have experience with ML to use it effectively.
  • Is browser-based only - there is no desktop application and it does not provide a API-first interface.
  • No enterprise features — no SSO, no audit logs, no RBAC
  • Very limited documentation — research tool vs. product
  • No managed hosting — user handles own infrastructure
  • Static analysis emphasis — does not allow for real-time monitoring

Who Is Google What-If Tool Best For?

Best For

  • ML researchers analyzing model behavior — Deeply interpretable (research focused) features for workflow usage
  • TensorFlow practitioners — Natively integrates with TensorFlow ecosystem & Colab
  • AI fairness researchers — Specialized for detecting bias & performing fairness analyses
  • Data science educators — Best suited to teach concepts around model interpretability
  • Budget-constrained teams — Extremely powerful analysis capabilities at no cost

Not Suitable For

  • Production MLOps teams — Does NOT provide for monitoring, alerting or enterprise functionality. Instead, use Arize or WhyLabs.
  • PyTorch or non-TensorFlow users — Only provides native framework support for a limited number of frameworks. Otherwise, consider using SHAP or Captum.
  • Non-technical business users — Requires ML expertise. For non-ML expert users, consider no-code solutions such as Fiddler AI.
  • Real-time inference monitoring — Intended for offline analysis; intended to be used in conjunction with production monitoring.

Are There Usage Limits or Geographic Restrictions for Google What-If Tool?

Framework Support
Primarily TensorFlow.js, limited other frameworks
Deployment
Browser-based or self-hosted, no managed service
Data Size
Browser memory limits for large datasets
Real-time Monitoring
Offline analysis only, no streaming data
Collaboration
No built-in sharing or team features
Model Formats
TensorFlow SavedModel, TF.js supported
Geographic Availability
Globally available as open-source
Compliance
No formal certifications, self-managed security

Is Google What-If Tool Secure and Compliant?

Open Source SecurityPublic GitHub repository allows community security review and auditing
Self-Hosted ControlUsers maintain complete control over data and deployment environment
Browser SecurityRuns in sandboxed browser environment with standard web security practices
TensorFlow SecurityLeverages battle-tested TensorFlow.js security model
No Data TransmissionAll analysis performed client-side, no data sent to external servers
Community AuditsActive GitHub issues and contributions enable rapid security fixes

What Customer Support Options Does Google What-If Tool Offer?

Channels
Community support via GitHub repositoryDiscussion forums for TensorFlow usersTagged questions under tensorflow and what-if-tool
Specialized
None - open source research tool
Business Tier
N/A - free open source tool
Support Limitations
•No official customer support or dedicated channels
•Community-driven support only, no guaranteed response times
•No phone, email, or live chat support available

What APIs and Integrations Does Google What-If Tool Support?

API Type
No public REST API. Integrates as TensorFlow plugin/embed in Jupyter/Colab/TensorBoard
Authentication
N/A - client-side JavaScript tool
Webhooks
Not applicable
SDKs
TensorFlow integration via witwidget Python package
Documentation
Good - official docs at pair-code.github.io/what-if-tool and TensorFlow.org/tensorboard
Sandbox
Live demos available in Colab notebooks and TensorBoard
SLA
N/A - open source tool, no uptime guarantees
Rate Limits
N/A - local/client-side execution
Use Cases
Embed in Jupyter/Colab for model debugging, fairness analysis, counterfactual exploration

What Are Common Questions About Google What-If Tool?

The What-If Tool is a visual interface to explore machine learning models. It allows investigation of how well models perform against the data; how well models perform across various sub-slices of the data; fairness metrics; and counterfactual examples. The What-If Tool supports TensorFlow models natively and others require only minimal code.

Works within Jupyter notebooks, Google Colab, TensorBoard and Cloud AI Platform Notebooks. It can also be easily embedded into applications with only a few lines of code by simply passing test data and the reference model.

The Performance + Fairness tab allows for slicing data by feature (i.e., race, gender, age) and comparing metrics across sub-groups. It will show disparate impact and allows for optimization of threshold values for different fairness constraint requirements.

Yes, it is completely free and is licensed under the Apache 2.0 License. There are no pricing tiers or subscription fees required.

Native support for TensorFlow classification and regression models. Native support exists for other frameworks through custom adapters. Support exists for binary classification, multi-class and regression.

Use GitHub Issues in the repository, TensorFlow Discussion Forums, or Stack Overflow with proper tags. There are no official support channels available.

Beginning of the Text (57). Load as many models as possible into the same session to be able to evaluate their performances on the same data set, and also to evaluate their fairness metrics and predictions side-by-side.

(58) Counterfactuals are those minimal changes to a datapoint that would flip a model's prediction. Therefore, you can select any datapoint and see the one that has the closest example that receives the opposite prediction.

Is Google What-If Tool Worth It?

(59) The Google What-If Tool is a great free and open source solution for ML practitioners who want to analyze their model behavior, debug predictions, and investigate fairness issues. The Google What-If Tool has several types of interactive visualizations for counterfactuals, partial dependence plots, and subgroup analysis that represent a large void in the typical workflow for ML practitioners. Although the Google What-If Tool is best suited for debugging/research purposes as opposed to real time monitoring of models in a production environment; the Google What-If Tool is the best way to interactively understand a model.

Recommended For

  • (60) ML engineers trying to debug model predictions and edge cases
  • (61) Data scientists investigating model fairness and bias
  • (62) AI researchers exploring counterfactual explanations
  • (63) Teams using TensorFlow that need fast model diagnostics
  • (64) Teachers that teach model interpretability and fairness

!
Use With Caution

  • (65) Production environments that require real-time monitoring
  • (66) Teams that need automated fairness reporting versus interactive exploration
  • (67) Users that use TensorFlow but do not have the ability to complete a lot of setup

Not Recommended For

  • (68) Companies that need enterprise support contracts
  • (69) Production MLOps teams that need API-based model monitoring
  • (70) Teams without ML engineering experience
Expert's Conclusion

(71) A necessary free tool for all ML practitioners who need to investigate how well their models perform and behave, and which can help them debug model predictions during the development stage.

Best For
(60) ML engineers trying to debug model predictions and edge cases(61) Data scientists investigating model fairness and bias(62) AI researchers exploring counterfactual explanations

What do expert reviews and research say about Google What-If Tool?

Key Findings

(72) Google What-If Tool is a mature, open-source visual interface for the exploration of ML models, fairness analysis and debugging of ML model predictions. Google What-If Tool is native to TensorFlow and runs seamlessly in Jupyter/Colab/TensorBoard, and provides very good capabilities for counterfactuals, subgroup fairness and partial dependence plots. There is no support channel, pricing, or enterprise features available with Google What-If Tool. It was created by Google PAIR as a research and engineering tool.

Data Quality

Excellent - comprehensive documentation from official Google sources (PAIR, TensorFlow.org). Active GitHub repo confirms maturity. No pricing/support info as expected for open source tool.

Risk Factors

!
(73) There are no official support channels or service level agreements (SLA).
!
This is limited to interactive exploration of model output, not real-time monitoring of model usage.
!
The primary focus of TensorFlow means that there will need to be significant adaptations to make it compatible with other frameworks.
Last updated: January 2026

What Are the Best Alternatives to Google What-If Tool?

  • •
    TensorBoard (What-If Plugin): The official TensorFlow suite of visualizations includes What-If Tool integrations. It provides a much broader view of the entire process of testing machine learning experiments, although is less focused on providing explanations related to fairness and counterfactuals. The best choice for existing TensorFlow users who are already utilizing TensorBoard.
  • •
    Facets (Google PAIR): Google’s companion tool for exploratory data analysis and visualization prior to model training. Provides additional information about the data quality in addition to what is provided by What-If. Is best used by data scientists performing data cleaning and preparation activities.
  • •
    SHAP: A popular python library for generating model explanations using SHAP values. Provides much more detailed information about feature importance than What-If or Why, however, is less interactive as a tool. The best option for companies requiring explanations from their models in order to use them in production environments.
  • •
    LIME: A library for creating Local Interpretable Model-agnostic Explanations. Creates local explanations similar to those created by What-If, however, they are developed through more coding activity. Is best for developers requiring model explanations in their custom ML pipeline development.
  • •
    Aequitas: A fairness audit toolkit, focused on measuring bias within model outputs and producing reports based upon this metric. Provides a more systematic approach to evaluating the fairness of model outputs, while being less interactive than Why. The best option for companies requiring formal evaluations of fairness and compliance reporting.

What Are Google What-If Tool's Fairness Metrics Framework?

0.95 %
Demographic Parity
0.92 %
Equal Opportunity
0.89 %
Calibration

What Bias Detection Capabilities Does Google What-If Tool Offer?

Interactive Model Visualization

An interactive web-based tool allowing practitioners to explore how model behavior varies by group and scenario, and allows practitioners to test how their model would perform in a variety of hypothetical situations and to visualize how their predictions change when presented with these new scenarios.

Partial Dependence Analysis

Demonstrates how changing each individual feature of a single datapoint affects the model’s prediction of that datapoint; provides an easy way to determine which features are driving the biased predictions of your model.

Counterfactual Analysis

Compares the selected datapoints against the datapoints that have the most similar attributes, yet were given different predictions by the model; demonstrates the minimum changes required to the input datapoints to affect the decision of the model.

Protected Attribute Analysis

Evaluates bias across protected characteristics; determines which demographic groups are experiencing disparate treatment in the predictions produced by the model

Dataset Slicing and Comparison

The ability to divide a data set by its features, and then compare how well each feature works to predict the desired outcome (model performance) in each slice, is used to determine what portions of the data set are being predicted best or worst, which can be useful in a fairness investigation.

Performance + Fairness Aggregation

Provides an overall picture of model performance for all of the data as a whole (not just individual slices), allowing a practitioner to get a "big-picture" view of both the fairness metrics for a given model, and how well that model performs on the overall data set.

Feature Importance Analysis

Identifies which input(s) contribute to model decision-making the most; this can help determine whether sensitive attributes or proxy-sensitive attributes have too much influence on a model's prediction.

Fairness Testing Framework

Allows support for five (5) different types of fairness metrics; enabling practitioners to assess and measure fairness from a variety of viewpoints.

What Is Google What-If Tool's Technical Integration Requirements?

Language Support
Python primary support; integration with TensorFlow and other ML frameworks
ML Framework Integration
Pre-installed in TensorFlow instances of Google Cloud AI Platform Notebooks; compatible with Google Cloud AI Platform deployment
Deployment Platforms
Integration with Google Cloud AI Platform; accessible through cloud-native infrastructure
Interactive Analysis Environment
WitWidget pre-installed in AI Platform Notebooks; web-based visualization interface for interactive exploration
Data Type Support
Support for structured data analysis; compatible with various model types
API Availability
Programmatic access through interactive widgets and cloud integration; accessible for integration into ML workflows

How Does Google What-If Tool's Primary Use Case Alignment Compare?

Use Case DomainSpecific ApplicationsRegulatory RequirementsBias Risk Level
Criminal JusticeRecidivism prediction, sentencing recommendations, risk assessment scoring (e.g., COMPAS algorithm)14th Amendment Equal Protection, state-level algorithmic accountability lawsCritical
Financial Services & LendingCredit decisions, mortgage approval, loan underwriting, premium pricingFair Credit Reporting Act (FCRA), Equal Credit Opportunity Act (ECOA), Dodd-FrankCritical
Hiring & EmploymentResume screening, candidate ranking, promotion decisionsEEOC guidelines, Title VII Civil Rights Act, ADACritical
Healthcare & InsuranceDiagnosis assistance, treatment recommendations, insurance underwritingCivil Rights Act Title VI, ADA, state insurance regulationsCritical
Model Development & GovernancePre-deployment audit, continuous production monitoring, model documentationEmerging AI governance standards, internal responsible AI policiesHigh

What Is Google What-If Tool's Compliance And Governance Framework Status?

NIST SP 1270: Identifying and Managing Bias in Artificial IntelligenceBias detection methodology documentation, metrics definition, impact assessment tools
Fair Lending Laws (FCRA, ECOA, Dodd-Frank)Disparate impact analysis, demographic parity reporting, model audit trails
EEOC Equal Employment Opportunity GuidelinesAdverse impact testing, selection rate analysis by protected class
Model Cards FrameworkBias analysis documentation, model limitations disclosure, demographic performance breakdowns
Audit Trail & Reproducibility RequirementsComplete logging of bias detection steps, reproducible results, stakeholder communication documentation

What Stakeholder Communication And Transparency Does Google What-If Tool Offer?

Interactive Web Dashboards

Google's What-If Tool offers users the capability to create interactive visualizations to allow for exploration of how a model behaves under various groupings and scenarios; allows stakeholders without technical backgrounds to grasp the findings of the bias analysis.

Visual Bias Analysis

Use confusion matrices and ROC curves to display how a model performs in terms of accuracy on different demographic groups; clearly shows where there may be performance differences and patterns of unfair treatment.

Fairness Metric Explanations

Five (5) different types of fairness metrics supported by the tool, along with descriptions explaining what each metric means and when it should be applied.

Feature Importance Visualization

Visual display of the features contributing the most to the model's decisions; assists in communicating which sensitive attributes or proxy-sensitive attributes contribute to biased predictions.

Counterfactual Explanations

Displays similar individuals that were treated differently; provides tangible examples of possible discrimination for stakeholder education/communication.

Bias Detection Summary Reports

Interactive exploration capabilities enable users to discover and document specific biases for reporting to stakeholders and regulatory bodies.

What Is Google What-If Tool's Enterprise Deployment And Scalability?

Cloud-Native Architecture
Integration with Google Cloud AI Platform for managed deployment; supports enterprise cloud infrastructure
Notebook Integration
WitWidget pre-installed in all TensorFlow instances of AI Platform Notebooks; seamless integration with data science workflows
Interactive Analysis
Web-based interface for exploring model behavior; no command-line tools required for stakeholder engagement
Multi-Dataset Support
Ability to analyze and compare multiple models; dataset slicing for comprehensive analysis across different data subsets
Version Control & Auditability
Documentation of fairness analysis methodology; support for reproducible bias detection workflows
API-First Architecture
Integration with Google Cloud AI Platform APIs; programmatic access to What-If Tool functionality
Performance Optimization
Efficient computation of fairness metrics across large datasets; aggregated reporting over entire dataset
Accessibility for Non-Technical Users
Interactive visualization eliminating need for data science expertise to understand bias findings; web-based interface

Expert Reviews

📝

No reviews yet

Be the first to review Google What-If Tool!

Write a Review

Similar Products

Fairlearn
fairlearn.org
IBM AI Fairness 360
aif360.mybluemix.net
Holistic AI
holisticai.com