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When Recom m endat ion Syst em s Go Bad
Evan Est ola 12/ 12/ 16
About Me
Evan Estola
Staff Machine Learning Engineer, Data Team Lead @ Meetup
@estola
We m ake com m unit y
real
268,000 Meetup Groups
28.8 Million Members
180 Countries
We w ant a w orld full of real, local com m unit y. W om e n’s V e te rans M e e tup, S an A ntonio , TX
Why Recs at Meet up are Hard
Cold Start
Sparsity
Lies
Recom m endat ion Syst em s: Collaborat ive Filt ering
Recom m endat ion Syst em s: Rat ing Predict ion
Netflix prize
How many stars would user X give movie Y
Boring
Recom m endat ion Syst em s: Learning To Rank
Active area of research
Use ML model to solve a ranking problem
Pointwise: Logistic Regression on binary label, use output for ranking
Listwise: Optimize entire list
Performance Metrics
Mean Average Precision
P@K
Discounted Cumulative Gain
Dat a Science im pact s
lives
Ads you see
Friend’s Activity/Facebook feed
News you’re exposed to
If a product is available
If you can get a ride
Price you pay for things
Admittance into college
Job openings you find
Job openings you can get
If you can get a loan
You just w ant ed a k it chen scale, now Am azon t hinks you’re a drug dealer
Ego
Member/customer/user first
Focus on building the best product,
not on being the most clever data
scientist
Much harder to spin a positive user
story than a story about how smart
you are
“Black-sounding” names 25% more
likely to be served ad suggesting
criminal record
Et hics
We have accepted that Machine Learning
can seem creepy, how do we prevent it
from becoming immoral?
We have an ethical obligation to not
teach machines to be prejudiced.
Dat a Et hics
Aw areness
Talk about it!
Identify groups that could be negatively
impacted by your work
Make a choice Take a stand
Int erpret ab le Models
For simple problems, simple solutions
are often worth a small concession
in performance
Inspectable models make it easier to
debug problems in data collection,
feature engineering etc.
Only include features that work the
way you want
Don’t include feature interactions that
you don’t want
Logist ic Regression
StraightDistanceFeature(-0.0311f),
ChapterZipScore(0.0250f),
RsvpCountFeature(0.0207f),
AgeUnmatchFeature(-1.5876f),
GenderUnmatchFeature(-3.0459f),
StateMatchFeature(0.4931f),
CountryMatchFeature(0.5735f),
FacebookFriendsFeature(1.9617f),
SecondDegreeFacebookFriendsFeature(0.1594f),
ApproxAgeUnmatchFeature(-0.2986f),
SensitiveUnmatchFeature(-0.1937f),
KeywordTopicScoreFeatureNoSuppressed(4.2432f),
TopicScoreBucketFeatureNoSuppressed(1.4469f,0.257f,10f),
TopicScoreBucketFeatureSuppressed(0.2595f,0.099f,10f),
ExtendedTopicsBucketFeatureNoSuppressed(1.6203f,1.091f,10f),
ChapterRelatedTopicsBucketFeatureNoSuppressed(0.1702f,0.252f,0.641f),
ChapterRelatedTopicsBucketFeatureNoSuppressed(0.4983f,0.641f,10f),
DoneChapterTopicsFeatureNoSuppressed(3.3367f)
Feat ure Engineering and Int eract ions
● Good Feature: ○ Join! You’re interested in Tech x Meetup is about Tech
● Good Feature: ○ Don’t join! Group is intended only for Women x You are a Man
● Bad Feature: ○ Don’t join! Group is mostly Men x You are a Woman
● Horrible Feature: ○ Don’t join! Meetup is about Tech x You are a Woman
Meetup is not interested in propagating gender stereotypes
Ensem ble Models and
Dat a segregat ion
Ensemble Models: Combine outputs of
several classifiers for increased accuracy
If you have features that are useful but
you’re worried about interaction (and
your model does it automatically) use
ensemble modeling to restrict the
features to separate models.
Ensem ble Model, Dat a Segregat ion
Data: *Interests Searches Friends Location
Data: *Gender Friends Location
Data: Model1 Prediction Model2 Prediction
Model1 Prediction
Model2 Prediction
Final Prediction
Fake profiles, track ads
Career coaching for “200k+” Executive
jobs Ad
Male group: 1852 impressions
Female group: 318
Diversit y Cont rolled Test ing
CMU - AdFisher
Crawls ads with simulated user profiles
Same technique can work to find bias in your own models!
Generate Test Data
Randomize sensitive feature in real data set
Run Model
Evaluate for unacceptable biased treatment
Must identify what features are sensitive and what outcomes are
unwanted
● Tw it t er bot ● “Garbage in,
garbage out ” ● Responsib ilit y?
“In the span of 15 hours Tay referred to feminism as a
"cult" and a "cancer," as well as noting "gender equality
= feminism" and "i love feminism now." Tweeting
"Bruce Jenner" at the bot got similar mixed response,
ranging from "caitlyn jenner is a hero & is a stunning,
beautiful woman!" to the transphobic "caitlyn jenner
isn't a real woman yet she won woman of the year?"”
Tay.ai
Diverse t est dat a
Outliers can matter
The real world is messy
Some people will mess with you
Some people look/act different than
you
Defense
Diversity Design
You know racist com put ers are a bad idea
Don’t let your com pany invent
racist com put ers
@estola
