1.
When
Recommendation
Systems Go Bad
Evan Estola
5/20/16

2.
About Me
Evan Estola
Lead Machine Learning Engineer @ Meetup
evan@meetup.com
@estola

3.
We want a world full of real, local community.
Women’s Veterans Meetup, San Antonio, TX

4.
Recommendation Systems: Collaborative Filtering

5.
Recommendation Systems: Rating Prediction
Netflix prize
How many stars would user X give movie Y
Boring

6.
Recommendation Systems: 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

7.
Data
Science
impacts
lives
Ads you see
Apps you download
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/get
If you can get a loan

8.
You just wanted a
kitchen scale, now
Amazon thinks you’re
a drug dealer

9.
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

10.
“Black-sounding” names 25% more
likely to be served ad suggesting
criminal record

11.
Ethics
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.

12.
Data
Ethics
Awareness
Tell your friends
Tell your coworkers
Tell your boss
Identify groups that could be
negatively impacted by your work
Make a choice
Take a stand

13.
Interpretable
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

14.
Logistic 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)

15.
Feature Engineering and Interactions
● 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

16.
Ensemble
Models and
Data
segregation
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.

17.
Ensemble Model, Data Segregation
Data:
*Interests
Searches
Friends
Location
Data:
*Gender
Friends
Location
Data:
Model1 Prediction
Model2 Prediction
Model1 Prediction
Model2 Prediction
Final Prediction

18.
Fake profiles, track ads
Career coaching for “200k+” Executive
jobs Ad
Male group: 1852 impressions
Female group: 318

19.
Diversity Controlled Testing
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

20.
● Twitter bot
● “Garbage in,
garbage out”
● Responsibility?
“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

21.
Diverse
test data
Outliers can matter
The real world is messy
Some people will mess with you
Some people look/act different than
you
Defense
Diversity
Design

22.
You know racist computers are a
bad idea
Don’t let your company invent
racist computers