Blog
The latest from Google Research
Equality of Opportunity in Machine Learning
Friday, October 7, 2016
Posted by Moritz Hardt, Research Scientist, Google Brain Team
As machine learning technology progresses rapidly, there is much interest in understanding its societal impact. A particularly successful branch of machine learning is
supervised learning
. With enough past data and computational resources, learning algorithms often produce surprisingly effective predictors of future events. To take one hypothetical example: an algorithm could, for example, be used to predict with high accuracy who will pay back their loan. Lenders might then use such a predictor as an aid in deciding who should receive a loan in the first place. Decisions based on machine learning can be both incredibly useful and have a profound impact on our lives.
Even the best predictors make mistakes. Although machine learning aims to minimize the chance of a mistake, how do we prevent certain groups from experiencing a disproportionate share of these mistakes? Consider the case of a group that we have relatively little data on and whose characteristics differ from those of the general population in ways that are relevant to the prediction task. As prediction accuracy is generally correlated with the amount of data available for training, it is likely that incorrect predictions will be more common in this group. A predictor might, for example, end up flagging too many individuals in this group as ‘high risk of default’ even though they pay back their loan. When group membership coincides with a sensitive attribute, such as race, gender, disability, or religion, this situation can lead to unjust or prejudicial outcomes.
Despite the need, a vetted methodology in machine learning for preventing this kind of discrimination based on sensitive attributes has been lacking. A naive approach might require a set of sensitive attributes to be removed from the data before doing anything else with it. This idea of “fairness through unawareness,” however, fails due to the existence of “redundant encodings.” Even if a particular attribute is not present in the data, combinations of other attributes can act as a proxy.
Another common approach, called
demographic parity
, asks that the prediction must be uncorrelated with the sensitive attribute. This might sound intuitively desirable, but the outcome itself is often correlated with the sensitive attribute. For example, the incidence of heart failure is substantially more common in men than in women. When predicting such a medical condition, it is therefore neither realistic nor desirable to prevent all correlation between the predicted outcome and group membership.
Equal Opportunity
Taking these conceptual difficulties into account, we’ve proposed a methodology for measuring and preventing discrimination based on a set of sensitive attributes. Our framework not only helps to scrutinize predictors to discover possible concerns. We also show how to adjust a given predictor so as to strike a better tradeoff between classification accuracy and non-discrimination if need be.
At the heart of our approach is the idea that individuals who qualify for a desirable outcome should have an equal chance of being correctly classified for this outcome. In our fictional loan example, it means the rate of ‘low risk’ predictions among people who actually pay back their loan should not depend on a sensitive attribute like race or gender. We call this principle
equality of opportunity
in supervised learning.
When implemented, our framework also improves incentives by shifting the cost of poor predictions from the individual to the decision maker, who can respond by investing in improved prediction accuracy. Perfect predictors always satisfy our notion, showing that the central goal of building more accurate predictors is well aligned with the goal of avoiding discrimination.
Learn more
To explore the ideas in this blog post on your own, our
Big Picture team
created a beautiful
interactive visualization
of the different concepts and tradeoffs. So, head on over to their page to learn more.
Once you’ve walked through the demo, please check out the
full version of our paper
, a joint work with Eric Price (UT Austin) and Nati Srebro (TTI Chicago). We’ll present the paper at this year’s Conference on Neural Information Processing Systems (
NIPS
) in Barcelona. So, if you’re around, be sure to stop by and chat with one of us.
Our paper is by no means the final word on this important and complex topic. It joins an ongoing conversation with a multidisciplinary focus of research. We hope to inspire future research that will sharpen the discussion of the different achievable tradeoffs surrounding discrimination and machine learning, as well as the development of tools that will help practitioners address these challenges.
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
materials science
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
Responsible AI
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
2022
May
Apr
Mar
Feb
Jan
2021
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
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
.
