Blog
The latest news from Google AI
The What-If Tool: Code-Free Probing of Machine Learning Models
Tuesday, September 11, 2018
Posted by James Wexler, Software Engineer, Google AI
Building effective machine learning (ML) systems means asking a lot of questions. It's not enough to train a model and walk away. Instead, good practitioners act as detectives, probing to understand their model better:
How would changes to a datapoint affect my model’s prediction? Does it perform differently for various groups–for example, historically marginalized people? How diverse is the dataset I am testing my model on?
Answering these kinds of questions isn’t easy. Probing “what if” scenarios often means writing custom, one-off code to analyze a specific model. Not only is this process inefficient, it makes it hard for non-programmers to participate in the process of shaping and improving ML models. One focus of the
Google AI PAIR initiative
is making it easier for a broad set of people to examine, evaluate, and debug ML systems.
Today, we are launching the
What-If Tool
, a new feature of the open-source
TensorBoard
web application, which let users analyze an ML model without writing code. Given pointers to a TensorFlow model and a dataset, the What-If Tool offers an interactive visual interface for exploring model results.
The What-If Tool, showing a set of 250 face pictures and their results from a model that detects smiles.
The What-If Tool has a large set of features, including visualizing your dataset automatically using
Facets
, the ability to manually edit examples from your dataset and see the effect of those changes, and automatic generation of partial dependence plots which show how the model’s predictions change as any single feature is changed. Let’s explore two features in more detail.
Exploring what-if scenarios on a datapoint.
Counterfactuals
With a click of a button you can compare a datapoint to the most similar point where your model predicts a different result. We call such points "counterfactuals," and they can shed light on the decision boundaries of your model. Or, you can edit a datapoint by hand and explore how the model’s prediction changes. In the screenshot below, the tool is being used on a binary classification model that predicts whether a person earns more than $50k based on public census data from the
UCI census dataset
. This is a benchmark prediction task used by ML researchers, especially when analyzing algorithmic fairness — a topic we'll get to soon. In this case, for the selected datapoint, the model predicted with 73% confidence that the person earns more than $50k. The tool has automatically located the most-similar person in the dataset for which the model predicted earnings of less than $50k and compares the two side-by-side. In this case, with just a minor difference in age and an occupation change, the model’s prediction has flipped.
Comparing counterfactuals.
Analysis of Performance and Algorithmic Fairness
You can also explore the effects of different classification thresholds, taking into account constraints such as different
numerical fairness criteria
. The below screenshot shows the results of a smile detector model, trained on the open-source
CelebA dataset
which consists of annotated face images of celebrities. Below, the faces in the dataset are divided by whether they have brown hair, and for each of the two groups there is an
ROC curve
and
confusion matrix
of the predictions, along with sliders for setting how confident the model must be before determining that a face is smiling. In this case, the confidence thresholds for the two groups were set automatically by the tool to optimize for
equal opportunity
.
Comparing the performance of two slices of data on a smile detection model, with their classification thresholds set to satisfy the “equal opportunity” constraint.
Demos
To illustrate the capabilities of the What-If Tool, we’ve released a set of demos using pre-trained models:
Detecting misclassifications:
A
multiclass classification model
, which predicts plant type from four measurements of a flower from the plant. The tool is helpful in showing the decision boundary of the model and what causes misclassifications. This model is trained with the
UCI iris dataset
.
Assessing fairness in binary classification models:
The
image classification model
for smile detection mentioned above. The tool is helpful in assessing algorithmic fairness across different subgroups. The model was purposefully trained without providing any examples from a specific subset of the population, in order to show how the tool can help uncover such biases in models. Assessing fairness requires careful consideration of the overall context — but this is a useful quantitative starting point.
Investigating model performance across different subgroups:
A regression model
that predicts a subject’s age from census information. The tool is helpful in showing relative performance of the model across subgroups and how the different features individually affect the prediction. This model is trained with the
UCI census dataset
.
What-If in Practice
We tested the What-If Tool with teams inside Google and saw the immediate value of such a tool. One team quickly found that their model was incorrectly ignoring an entire feature of their dataset, leading them to fix a previously-undiscovered code bug. Another team used it to visually organize their examples from best to worst performance, leading them to discover patterns about the types of examples their model was underperforming on.
We look forward to people inside and outside of Google using this tool to better understand ML models and to begin assessing fairness. And as the code is open-source, we welcome contributions to the tool.
Acknowledgments
The What-If Tool was a collaborative effort, with UX design by Mahima Pushkarna, Facets updates by Jimbo Wilson, and input from many others. We would like to thank the Google teams that piloted the tool and provided valuable feedback and the TensorBoard team for all their help.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2021
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
