Consequences of an Insightful Algorithm

@ C C Z O N A
C O N S E Q U E N C E S O F A N
I N S I G H T F U L A L G O R I T H M
C A R I N A C . Z O N A
@ C C Z O N A

@ C C Z O N A
infertility
sex shaming
miscarriage
bullying
stalking 
grief
PTSD
segregation
racial profiling
white supremacy
holocaust
CONTENT WARNING

@ C C Z O N A
ALGORITHMS  
IMPOSE
CONSEQUENCES  
ON PEOPLE  
ALL THE TIME

@ C C Z O N A
PATTERNS OF
INSTRUCTIONS,
ARTICULATED IN
CODE OR FORMULAS
A L G O R I T H M S O F C O M P U T E R S C I E N C E & M AT H E M AT I C S

@ C C Z O N A
STEP-BY-STEP SET OF
OPERATIONS FOR  
PREDICTABLY ARRIVING
AT AN OUTCOME
A L G O R I T H M

@ C C Z O N A
PATTERNS OF
INSTRUCTIONS,
ARTICULATED IN
WAYS SUCH AS…
A L G O R I T H M S I N O R D I N A RY L I F E

@ C C Z O N A
Ch 12, sl st to join. Rnd 1: ch 3, 17 dc. Rnd 2: ch 3, 1 dc, ch 4,
skip 1 dc, *2 dc, ch 4, skip 1 dc*, repeat 5 times, sl st. Rnd 3:
ch 3, 2 dc, ch 5, skip 2 ch and 1 dc, *3 dc, ch 5, skip 2 ch and
1 dc*, repeat 5 times, sl st. Rnd 4: ch 3, 3 dc, ch 5, skip 3 ch
and 1 dc, *4 dc, ch 5, skip 3 ch and 1 dc*, repeat 5 times, sl
st. Rnd 5: ch 3, 4 dc, ch 6, skip 3 ch and 1 dc, *5 dc, ch 6, skip
3 ch and 1 dc*, repeat 5 times, sl st. Rnd 6: ch 3, 6 dc, ch 6,
skip 3 ch and 1 dc, *7 dc, ch 6, skip 3 ch and 1 dc*, repeat 5
times, sl st. Rnd 7: ch 3, 8 dc, ch 6, skip 3 ch and 1 dc, *9 dc,
ch 6, skip 3 ch and 1 dc*, repeat 5 times, sl st. Rnd 8: ch 3, 10
dc, ch 6, skip 3 ch and 1 dc, *11 dc, ch 6, skip 3 ch and 1 dc*,
repeat 5 times, sl st .Rnd 9: ch 3, 12 dc, ch 6, skip 3 ch and 1
dc, *13 dc, ch 6, skip 3 ch and 1 dc*, repeat 5 times, sl st.
Rnd 10: ch 3, 14 dc, ch 7, skip 3 ch and 1 dc, *15 dc, ch 7,
skip 3 ch and 1 dc*, repeat 5 times, sl st. Rnd 11: ch 3, 16 dc,
ch 7, skip 4 ch and 1 dc, *17 dc, ch 7, skip 4 ch and 1 dc*,
repeat 5 times, sl st. Rnd 12: ch 3, 18 dc, ch 8, skip 4 ch and 1
dc, *19 dc, ch 8, skip 4 ch and 1 dc*, repeat 5 times, sl st.
Rnd 13: ch 3, 20 dc, ch 8, skip 5 ch and 1 dc, *21 dc, ch 8,
skip 5 ch and 1 dc*, repeat 5 times, sl st. Rnd 14: ch 3, 16 dc,
ch 8, skip 3 dc and 2 ch, 1 dc, ch 8, skip 1 dc, *17 dc, ch 8,
skip 3 dc and 2 ch, 1 dc, ch 8, skip 1 dc,* repeat 5 times, sl st.

@ C C Z O N A
The exhaustive rendering of our
conscious and unconscious patterns
into data sets and algorithms.
P R E D I C T I V E A N A LY T I C S
—Charles Duhigg

@ C C Z O N A
ALGORITHMS FOR
FAST, TRAINABLE,
ARTIFICIAL NEURAL
NETWORKS
D E E P L E A R N I N G

@ C C Z O N A
INPUT
Words, images, sounds,
objects, or abstract concepts
EXECUTION
Run a series of functions
repeatedly in a black box
OUTPUT
Prediction of properties useful
for drawing intuitions about
similar future inputs

@ C C Z O N A
ARTIFICIAL NEURAL NETWORK'S
AUTOMATED DISCOVERY
OF PATTERNS WITHIN  
A TRAINING DATASET
APPLIES DISCOVERIES  
TO DRAW INTUITIONS  
ABOUT FUTURE DATA
D E E P L E A R N I N G

@ C C Z O N A
– P e t e Wa rd e n
" T H I N K O F T H E M A S  
D E C I S I O N - M A K I N G  
B L A C K B O X E S . "

@ C C Z O N A
Medical Diagnosing
Pharmaceutical Development
Emotion Detection
Predictive Text
Face Identification
Voice-Activated Commands
Fraud Detection
Sentiment Analysis
Translating Text
Video Content Recognition
Self-Driving Cars

@ C C Z O N A
Ad Targeting
Behavioral Prediction
Recommendation Systems
Image Classification
Face Recognition

Algorithmic
Profiling
De-
Anonymization
Black Box
Disparate
Impact
Consent 
Issues
Replicates 
Bias
Personally
Identifiable Info
Human
Complexity Fail
Unproven
Methods
Inadvertent
Algorithmic
Cruelty
Uncritical
Assumptions
False 
Neutrality
High Risk
Consequences
Deception Filtering
Moved Fast,  
Broke Things
Invasion Of
Privacy
No Recourse
Creepy 
Stalkery
Messing With
Heads
Not How Valid
Research Works
Data 
Insecurity
Accurate, But  
Not Right
Shaming
Diversity 
Fail
#DataMiningFail

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🔵Algorithmic
Profiling
De-
Anonymization
Black Box
Disparate
Impact
Consent 
Issues
Replicates 
Bias
Personally
Identifiable Info
Human
Complexity Fail
Unproven
Methods
Inadvertent
Algorithmic
Cruelty
Uncritical
Assumptions
False 
Neutrality
High Risk
Consequences
Deception Filtering
Moved Fast,  
Broke Things
Invasion Of
Privacy
No Recourse
Creepy 
Stalkery
Messing With
Heads
Not How Valid
Research Works
Data 
Insecurity
Accurate, But  
Not Right
Shaming
Diversity 
Fail
TA R G E T

@ C C Z O N A
“IF WE WANTED TO FIGURE OUT 
IF A CUSTOMER IS PREGNANT,
EVEN IF SHE
DIDN’T WANT US TO KNOW,  
CAN YOU DO THAT? ”

@ C C Z O N A
‟As long as a pregnant
woman thinks she hasn’t
been spied on…  
as long as we don’t
spook her, it works.”

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Algorithmic
Profiling
De-
Anonymization
Black Box
Disparate
Impact
Consent 
Issues
Replicates 
Bias
Personally
Identifiable Info
Human
Complexity Fail
Unproven
Methods
Inadvertent
Algorithmic
Cruelty
Uncritical
Assumptions
False 
Neutrality
High Risk
Consequences
Deception Filtering
Moved Fast,  
Broke Things
Invasion Of
Privacy
No Recourse
Creepy 
Stalkery
Messing With
Heads
Not How Valid
Research Works
Data 
Insecurity
Accurate, But  
Not Right
Shaming
Diversity 
Fail
S H U T T E R F LY

@ C C Z O N A
‟Thanks, Shutterfly, for  
the congratulations on my
'new bundle of joy'.  
I'm horribly infertile,
but hey,
I'm adopting  
a kitten, 
so...”

@ C C Z O N A
‟ I l o s t a b a b y i n
N o v e m b e r w h o
w o u l d h a v e b e e n
d u e t h i s w e e k .
I t w a s l i k e  
h i t t i n g a w a l l  
a l l o v e r
a g a i n … ˮ

@ C C Z O N A
‟ T H E I N T E N T O F T H E  
E M A I L WA S T O TA R G E T
C U S T O M E R S W H O
H AV E R E C E N T LY
H A D A B A B Y "

@ C C Z O N A
‟You start imagining who  
they'll become and dreaming  
of hopes for their future.
You start making plans.
And then they're gone.
It's a lonely experience."

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Algorithmic
Profiling
De-
Anonymization
Black Box
Disparate
Impact
Consent 
Issues
Replicates 
Bias
Personally
Identifiable Info
Human
Complexity Fail
Unproven
Methods
Inadvertent
Algorithmic
Cruelty
Uncritical
Assumptions
False 
Neutrality
High Risk
Consequences
Deception Filtering
Moved Fast,  
Broke Things
Invasion Of
Privacy
No Recourse
Creepy 
Stalkery
Messing With
Heads
Not How Valid
Research Works
Data 
Insecurity
Accurate, But  
Not Right
Shaming
Diversity 
Fail
FA C E B O O K  
Y E A R I N R E V I E W

@ C C Z O N A
The result of code that
works in the overwhelming
majority of cases but
doesn’t take other use
cases into account.
I N A D V E R T E N T A L G O R I T H M I C C R U E LT Y
—Eric Meyer

@ C C Z O N A
‟ I N C R E A S E A WA R E N E S S O F  
A N D C O N S I D E R AT I O N F O R  
T H E FA I L U R E M O D E S  
T H E E D G E C A S E S  
T H E W O R S T- C A S E
S C E N A R I O S
– E r i c M e y e r

@ C C Z O N A
BEHUMBLE.WECANNOTINTUIT 
INNERSTATE,EMOTIONS, 
PRIVATESUBJECTIVITY
— C a r i n a C . Z o n a

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Algorithmic
Profiling
De-
Anonymization
Black Box
Disparate
Impact
Consent 
Issues
Replicates 
Bias
Personally
Identifiable Info
Human
Complexity Fail
Unproven
Methods
Inadvertent
Algorithmic
Cruelty
Uncritical
Assumptions
False 
Neutrality
High Risk
Consequences
Deception Filtering
Moved Fast,  
Broke Things
Invasion Of
Privacy
No Recourse
Creepy 
Stalkery
Messing With
Heads
Not How Valid
Research Works
Data 
Insecurity
Accurate, But  
Not Right
Shaming
Diversity 
Fail
F I T B I T

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Algorithmic
Profiling
De-
Anonymization
Black Box
Disparate
Impact
Consent 
Issues
Replicates 
Bias
Personally
Identifiable Info
Human
Complexity Fail
Unproven
Methods
Inadvertent
Algorithmic
Cruelty
Uncritical
Assumptions
False 
Neutrality
High Risk
Consequences
Deception Filtering
Moved Fast,  
Broke Things
Invasion Of
Privacy
No Recourse
Creepy 
Stalkery
Messing With
Heads
Not How Valid
Research Works
Data 
Insecurity
Accurate, But  
Not Right
Shaming
Diversity 
Fail
U B E R

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Algorithmic
Profiling
De-
Anonymization
Black Box
Disparate
Impact
Consent 
Issues
Replicates 
Bias
Personally
Identifiable Info
Human
Complexity Fail
Unproven
Methods
Inadvertent
Algorithmic
Cruelty
Uncritical
Assumptions
False 
Neutrality
High Risk
Consequences
Deception Filtering
Moved Fast,  
Broke Things
Invasion Of
Privacy
No Recourse
Creepy 
Stalkery
Messing With
Heads
Not How Valid
Research Works
Data 
Insecurity
Accurate, But  
Not Right
Shaming
Diversity 
Fail
G O O G L E
A D W O R D S

@ C C Z O N A
Amy
Claire
Emma
Katelyn
Katie
Madeline
Molly
Aaliyah
Diamond
Ebony
Imani
Latanya
Nia
Shanice
Cody
Connor
Dustin
Jack
Luke
Tanner
Wyatt
Darnell
DeShawn
Malik
Marquis
Terrell
Trevon
Tyrone
G O O G L E A D W O R D S

PLACEHOLDER@ C C Z O N A
PLACEHOLDER
PLACEHOLDERA BLACK IDENTIFYING NAME WAS  
25% MORE LIKELY 
TO RESULT IN AN AD THAT I M P L I E D A N  
ARREST RECORD

@ C C Z O N A

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🔵Algorithmic
Profiling
De-
Anonymization
Black Box
Disparate
Impact
Consent 
Issues
Replicates 
Bias
Personally
Identifiable Info
Human
Complexity Fail
Unproven
Methods
Inadvertent
Algorithmic
Cruelty
Uncritical
Assumptions
False 
Neutrality
High Risk
Consequences
Deception Filtering
Moved Fast,  
Broke Things
Invasion Of
Privacy
No Recourse
Creepy 
Stalkery
Messing With
Heads
Not How Valid
Research Works
Data 
Insecurity
Accurate, But  
Not Right
Shaming
Diversity 
Fail

@ C C Z O N A
Classifying people as “similar”,
where careless prompts create
scenarios harder to control  
and prepare for.
A C C I D E N TA L A L G O R I T H M I C R U N - I N S
—adapted from Joanne McNeil

@ C C Z O N A
A L G O R I T H M I C H U B R I S

@ C C Z O N A
"If you are stalked by a former
coworker, Twitter may reinforce
this connection, algorithmically
boxing you into a past while
you are trying to move on.  
Your affinity score with your
harasser will grow higher with
every person who follows this
person at Twitter’s
recommendation."
T W I T T E R - W H O T O F O L L O W

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Algorithmic
Profiling
De-
Anonymization
Black Box
Disparate
Impact
Consent 
Issues
Replicates 
Bias
Personally
Identifiable Info
Human
Complexity Fail
Unproven
Methods
Inadvertent
Algorithmic
Cruelty
Uncritical
Assumptions
False 
Neutrality
High Risk
Consequences
Deception Filtering
Moved Fast,  
Broke Things
Invasion Of
Privacy
No Recourse
Creepy 
Stalkery
Messing With
Heads
Not How Valid
Research Works
Data 
Insecurity
Accurate, But  
Not Right
Shaming
Diversity 
Fail

@ C C Z O N A
I P H O T O FA C E D E T E C T I O N

@ C C Z O N A
M I C R O S O F T H O W - O L D . N E T

@ C C Z O N A
F L I C K R M A G I C V I E W A U T O - TA G G I N G

@ C C Z O N A
G O O G L E P H O T O S A U T O - C AT E G O R I Z I N G

@ C C Z O N A
S H I R L E Y C A R D S

@ C C Z O N A
The tools used to make film,
b y M o n t ré A z a M i s s o u r i
@ C C Z O N A
not
are
the science of it,
racially
neutral
S H I R L E Y C A R D S

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Algorithmic
Profiling
De-
Anonymization
Black Box
Disparate
Impact
Consent 
Issues
Replicates 
Bias
Personally
Identifiable Info
Human
Complexity Fail
Unproven
Methods
Inadvertent
Algorithmic
Cruelty
Uncritical
Assumptions
False 
Neutrality
High Risk
Consequences
Deception Filtering
Moved Fast,  
Broke Things
Invasion Of
Privacy
No Recourse
Creepy 
Stalkery
Messing With
Heads
Not How Valid
Research Works
Data 
Insecurity
Accurate, But  
Not Right
Shaming
Diversity 
Fail

@ C C Z O N A
A L G O R I T H M S A R E G AT E K E E P E R S
Credit Housing Jobs

@ C C Z O N A
IF A MACHINE IS EXPECTED TO BE
INFALLIBLE
IT CANNOT ALSO BE
INTELLIGENT— A l a n Tu r i n g

@ C C Z O N A
!=I R L F R I E N D S FA C E B O O K F R I E N D S

@ C C Z O N A
F R I E N D S
F I N A N C E S
— i l l u s t r a t i o n b y S u s i e C a g l e

@ C C Z O N A
UNDERLYING
ASSUMPTIONS
INFLUENCE
OUTCOMES
AND
CONSEQUENCES @ C C Z O N A

@ C C Z O N A
— H e n r y D a v i d T h o re a u
“ T H E U N I V E R S E I S W I D E R
T H A N O U R V I E W S O F I T ”
@ C C Z O N A

@ C C Z O N A
OUR INDUSTRY IS  
IN AN ARMS RACE

@ C C Z O N A
Amazon
Apple
AT&T
Baidu
DARPA
Dropbox
eBay
Facebook
Flickr
Google
IBM
Microsoft
Netflix
Pandora
PayPal
Pinterest
Skype
Snapchat
Spotify
Tesla
Twitter
Yahoo
Uber

@ C C Z O N A
MOREPRECISE 
INCORRECTNESS
MORE 
DAMAGING 
INWRONGNESS

@ C C Z O N A
FLIPPING THE
PARADIGM

@ C C Z O N A
CONSIDER
DECISIONS'
POTENTIAL
IMPACTS
F L I P P I N G T H E PA R A D I G M
A C M C o d e o f E t h i c s
1

@ C C Z O N A
•PROJECT THE LIKELIHOOD OF
CONSEQUENCES
•MINIMIZE NEGATIVE
CONSEQUENCES
E VA L U AT E P O T E N T I A L I M PA C T S

@ C C Z O N A
FIRST DO NO
HARM

@ C C Z O N A
BE HONEST
BE TRUSTWORTHY
2

@ C C Z O N A
BUILD IN
RECOURSE

@ C C Z O N A
FULLY DISCLOSE
LIMITATIONS.
CALL ATTENTION TO
SIGNS OF RISK OF HARM.
3

@ C C Z O N A
BE VISIONARY
ABOUT
COUNTERING BIAS
4

@ C C Z O N A
ANTICIPATE
DIVERSE WAYS
TO SCREW UP
E M PAT H E T I C C O D E R S
5

@ C C Z O N A
W E M U S T H A V E
D E C I S I O N - M A K I N G
A U T H O R I T Y  
I N T H E H A N D S O F
H I G H LY  
D I V E R S E  
T E A M S

@ C C Z O N A
The point of culture fit is to
avoid disruption of groupthink

@ C C Z O N A
Unidimensional variety 
is not diversity

@ C C Z O N A
AUDIT
OUTCOMES 
CONSTANTLY
6

@ C C Z O N A
AUDIT OUTCOMES CONSTANTLY

@ C C Z O N A
1,852318
" C A R E F U L LY C H O S E N T R A I L S A C R O S S T H E W E B  
I N S U C H A WAY T H AT A N A D - TA R G E T I N G N E T W O R K
W I L L I N F E R C E RTA I N I N T E R E S T S O R A C T I V I T I E S . "
A D F I S H E R

@ C C Z O N A
C R O W D K N O W L E D G E > = B L A C K B O X I N T U I T I O N S
7C R O W D S O U R C E A L L T H E
T H I N G S

@ C C Z O N A
CULTIVATE
INFORMED
CONSENT
8

@ C C Z O N A
"LIST THINGS YOU  
DON'T WANT  
TO THINK ABOUT"

@ C C Z O N A
Opt-Out Opt-In
Block Follow
Ignore View
Filter Select
Disable Enable
S T O P T H E A L G O R I T H M B E T H E A L G O R I T H M

@ C C Z O N A
"Why not let Twitter users
select the people they
recommend you follow?
We already have Follow
Friday." —adapted from Joanne McNeil & Melissa Gira Grant

@ C C Z O N A
Would you like
special coupons for
pregnancy & infant
care?

@ C C Z O N A
COMMIT TO DATA
TRANSPARENCY.
COMMIT TO ALGORITHMIC
TRANSPARENCY.
9

@ C C Z O N A
We DO NOT BUILD here  
without understanding
CONSEQUENCES
TO OTHERS

@ C C Z O N A
PROFESSIONALS 
APPLY EXPERTISE &
JUDGEMENT
ABOUT  
HOW TO  
SOLVE PROBLEMS

Algorithmic
Profiling
De-
Anonymization
Black Box
Disparate
Impact
Consent 
Issues
Replicates 
Bias
Personally
Identifiable Info
Human
Complexity Fail
Unproven
Methods
Inadvertent
Algorithmic
Cruelty
Uncritical
Assumptions
False 
Neutrality
High Risk
Consequences
Deception Filtering
Moved Fast,  
Broke Things
Invasion Of
Privacy
No Recourse
Creepy 
Stalkery
Messing With
Heads
Not How Valid
Research Works
Data 
Insecurity
Accurate, But  
Not Right
Shaming
Diversity 
Fail
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@ C C Z O N A
R E F U S E T O  
P L AY A L O N G .

@ C C Z O N A
T H A N K Y O U .

@ C C Z O N A
Code Newbies
http://www.codenewbie.org/podcast/algorithms
“CONSEQUENCES OF AN INSIGHTFUL ALGORITHM”
CONTINUES AT:
Ruby Rogues
https://devchat.tv/ruby-rogues/278-rr-consequences-of-an-
insightful-algorithm-with-carina-c-zona

@ C C Z O N A
CASE STUDIES
FitBit: http://thenextweb.com/insider/2011/07/03/fitbit-users-are-inadvertently-
sharing-details-of-their-sex-lives-with-the-world/
Uber: http://www.forbes.com/sites/kashmirhill/2014/10/03/god-view-uber-
allegedly-stalked-users-for-party-goers-viewing-pleasure/ & http://
valleywag.gawker.com/uber-allegedly-used-god-view-to-stalk-vip-users-as-
a-1642197313 & http://www.forbes.com/sites/chanellebessette/2014/11/25/does-
uber-even-deserve-our-trust/ & http://www.wired.com/2011/04/app-stars-uber/ &
http://motherboard.vice.com/read/ubers-god-view-was-once-available-to-drivers
& http://motherboard.vice.com/read/ubers-god-view-was-once-available-to-
drivers
OkCupid: http://blog.okcupid.com/index.php/the-best-questions-for-first-dates/
Afﬁrm, et al: http://recode.net/2015/05/06/max-levchins-affirm-raises-275-
million-to-make-loans/ & http://time.com/3430817/paypal-levchin-affirm-lending/
& http://www.nytimes.com/2015/01/19/technology/banking-start-ups-adopt-
new-tools-for-lending.html & http://www.spiegel.de/international/germany/
critique-of-german-credit-agency-plan-to-mine-facebook-for-data-a-837713.html
& http://www.motherjones.com/politics/2013/09/lenders-vet-borrowers-social-
media-facebook
Shutterﬂy: http://jezebel.com/shutterfly-thinks-you-just-had-a-baby-1576261631
& http://www.adweek.com/adfreak/shutterfly-congratulates-thousands-women-
babies-they-didnt-have-157675
Zuckerberg on miscarriage: https://www.facebook.com/photo.php?
fbid=10102276573729791
Target: http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html
Facebook Year in Review: http://meyerweb.com/eric/thoughts/2014/12/24/
inadvertent-algorithmic-cruelty/ & http://www.theguardian.com/technology/
2014/dec/29/facebook-apologises-over-cruel-year-in-review-clips
Google AdWords: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2208240
& http://freakonomics.com/2013/04/08/how-much-does-your-name-matter-full-
transcript/
Flickr: http://www.theguardian.com/technology/2015/may/20/flickr-complaints-
offensive-auto-tagging-photos
Google Photos: https://twitter.com/jackyalcine/status/615329515909156865 &
https://www.yahoo.com/tech/google-photos-mislabels-two-black-americans-
as-122793782784.html
Twitter Who To Follow & Last.fm Similar Listeners: https://medium.com/
message/harassed-by-algorithms-f2b8229488df
Chicken Breast: http://metro.co.uk/2016/01/27/this-chicken-breast-has-a-
surprisingly-healthy-heart-rate-considering-its-dead-5647836/ & https://
www.youtube.com/watch?v=7rO5knyjDR0
LinkedIn http://www.slate.com/articles/technology/technology/2016/05/
linkedin_called_me_a_white_supremacist.html
Shirley Cards, Race, & Film Emulsion: http://priceonomics.com/how-
photography-was-optimized-for-white-skin/ & http://www.buzzfeed.com/
syreetamcfadden/teaching-the-camera-to-see-my-skin#.wkv3Jmd8O

@ C C Z O N A
RESOURCES
• Association for Computing Machinery Code
of Ethics http://www.acm.org/about/code-
of-ethics
• The 10 Commandments of Egoless
Programming http://www.techrepublic.com/
article/the-ten-commandments-of-egoless-
programming-6353837/
• Disparate Impact Analysis is Key to Ensuring
Fairness in the Age of the Algorithm http://
www.datainnovation.org/2015/01/disparate-
impact-analysis-is-key-to-ensuring-fairness-
in-the-age-of-the-algorithm/
• On Algorithmic Fairness, Discrimination and
Disparate impact. http://
fairness.haverford.edu/
• Big Data's Disparate Impact http://
papers.ssrn.com/sol3/papers.cfm?
abstract_id=2477899
• Algorithmic Accountability & Transparency
http://www.nickdiakopoulos.com/projects/
algorithmic-accountability-reporting/
• What is Deep Learning and why should you
care? http://radar.oreilly.com/2014/07/what-
is-deep-learning-and-why-should-you-
care.html
• Facebook Research | Machine Learning
https://research.facebook.com/
machinelearning

@ C C Z O N A
LAW, GOVERNANCE, & POLICYMAKING
• AI is Setting Up the Internet for a Huge
Class with Europe (2016) https://
www.wired.com/2016/07/artificial-
intelligence-setting-internet-huge-clash-
europe/
• California Law Review (2016): Big Data's
Disparate Impact http://papers.ssrn.com/
sol3/papers.cfm?abstract_id=2477899
• United States Federal Trade Commission
Report (2016) Big Data: A Tool for Inclusion
or Exclusion? https://www.ftc.gov/system/
files/documents/reports/big-data-tool-
inclusion-or-exclusion-understanding-
issues/160106big-data-rpt.pdf
• White House Big Data Privacy Report
(2014) https://www.whitehouse.gov/sites/
default/files/docs/
big_data_privacy_report_may_1_2014.pdf
• Algorithmic Transparency and Platform
Loyalty or Fairness in the French Digital
Republic Bill (2016) http://blogs.lse.ac.uk/
mediapolicyproject/2016/04/22/algorithmic-
transparency-and-platform-loyalty-or-
fairness-in-the-french-digital-republic-bill/
• European Data Protection Supervisor
Opinion (2015): Meeting the Challenges of
Big Data https://secure.edps.europa.eu/
EDPSWEB/webdav/site/mySite/shared/
Documents/Consultation/Opinions/
2015/15-11-19_Big_Data_EN.pdf

@ C C Z O N A
FLICKR CREATIVE COMMONS
Cookie face: https://www.flickr.com/photos/amayu/4462907505/in/
pool-977532@N24/
Eye shadows: https://www.flickr.com/photos/niallb/5300259686/
Colored pencils: https://www.flickr.com/photos/jenson-lee/
6315443914/
Audit keyboard: https://www.flickr.com/photos/jakerust/16811751576/
Crystal balls: https://www.flickr.com/photos/cmogle/3348401259/
Eye: https://www.flickr.com/photos/weirdcolor/3312989961/
Sea Wall: https://www.flickr.com/photos/christing/1447039348/
Door: https://www.flickr.com/photos/chrisschoenbohm/14181173109/
WTF Was This WTF Was That by Birger King: https://www.flickr.com/
photos/birgerking/7080728907/
Tasveer ByTaru: https://www.flickr.com/photos/tasveerbytaru/
19547308031/sizes/h/
Boxing day: https://www.flickr.com/photos/erix/6306715646/
Printing machine by Teddy Kwok: https://www.flickr.com/photos/
bblkwok/8247948713/
Caduceus: https://www.flickr.com/photos/spirit-fire/4832779325/
wocintech (microsoft) - 69 by #wocintech: https://www.flickr.com/
photos/wocintechchat/25926648871/in/album-72157664006621903/
ねこ　-ちゃーちゃん- by atacamaki: https://www.flickr.com/photos/
mikey-a-tucker/14792307898/
Driving home by Christian Weidinger: https://www.flickr.com/photos/
ch-weidinger/13888995404/
Server graphs by Aaron Parecki: https://www.flickr.com/photos/
aaronpk/5967579738/
On the Banks of Loch Linnhe by Henry Hemming: https://
www.flickr.com/photos/henry_hemming/8280971407/
Scaffolding by ajjyt: https://www.flickr.com/photos/
54588550@N08/9387947434/
Monarchial Scrutiny by Joel Penner: https://www.flickr.com/photos/
featheredtar/2949748121/
Canadian Money $100 Dollar Bill Macro of Robert Borden by Wilson
Hui: https://www.flickr.com/photos/wilsonhui/6604606217/
The grindstone by Kathryn Decker: https://www.flickr.com/photos/
waponigirl/5621810815/
"img049" by Steve Walker Photography: https://www.flickr.com/
photos/stover98074/11361095594/
Street Passion by Johnny Silvercloud: https://www.flickr.com/photos/
johnnysilvercloud/15718876258/
Buttons: https://www.flickr.com/photos/48462557@N00/3616793654/

@ C C Z O N A
NOUN PROJECT CREATIVE COMMONS
Businesspeople by Honnos Bondor 
https://thenounproject.com/term/businesspeople/17542/
Send by David Papworth 
https://thenounproject.com/term/send/135469/
Users by Lorena Salagre 
https://thenounproject.com/term/users/32253/
Friend by Megan Mitchel 
https://thenounproject.com/term/friend/6808/
Woman (1) by Thomas Helbig 
https://thenounproject.com/term/woman/120381/
Woman (2) by Thomas Helbig 
https://thenounproject.com/term/woman/120380/
Couple by Shankar Narayan 
https://thenounproject.com/term/couple/1014/
Couple by Pasquale Cavorsi 
https://thenounproject.com/term/couple/33095/
Check mark by Kris Brauer 
https://thenounproject.com/term/check-mark/192830/
Job Seeker by Stijn Elskens 
https://thenounproject.com/term/job-seeker/226871/
Credit Card by Oliviu Stoian 
https://thenounproject.com/term/credit-card/269411/
Single House by Aaron K. Kim 
https://thenounproject.com/term/single-house/123924/
Downloads by Icons8 
https://thenounproject.com/term/downloads/61761/
Id Card (1) by Boudewijn Mijnlieff 
https://thenounproject.com/term/id-card/203566/
Id Card (2) by Boudewijn Mijnlieff 
https://thenounproject.com/term/id-card/203578/
Cancel by Kris Brauer 
https://thenounproject.com/term/cancel/182501/

@ C C Z O N A
ADDITIONAL IMAGES
Recipe cards: Olivia Juice & Co. http://
olivejuiceco.typepad.com/my_weblog/2006/04/
easter_recap_an.html
Target circulars: http://flyers.smartcanucks.ca/uploads/
pages/26431/target-canada-weekly-flyer-july-11-
to-1713.jpg
Crochet pattern & shawl: http://www.abc-knitting-
patterns.com/1129.html
Space Invaders: http://www.caffination.com/
backchannel/shooting-for-the-high-score-3942/
Facebook b/w logo sketch: http://connect-
communicate-change.com/wp-content/uploads/
2011/11/facebook-logo-square-webtreatsetc.png
Stephen Hawking by CERN/Lawrent Egli: https://
www.facebook.com/stephenhawking/photos/
710802829006817
Jonathon Arrears by Susie Cagle: https://twitter.com/
susie_c/status/631619118865604610/photo/1
Twitter keyboard: http://www.npr.org/sections/
itsallpolitics/2012/05/04/152028258/navigating-politics-
on-twitter-some-npr-followfriday-suggestions
Tesla's Autopilot System MobilEye http://
wccftech.com/tesla-autopilot-story-in-depth-
technology/
Uber home screen https://www.supermoney.com/
2016/05/5-reasons-uber-can-risky-choice-drivers-
passengers/
Walden Pond by Walden Pond State Reservation
https://waldenpondstatereservation.wordpress.com/
The Force is Strong With This One by Mark Zuckerberg
https://www.facebook.com/zuck/posts/
10102531565693851
Bubble-sort with Hungarian ("Csángó") folk dance
https://www.youtube.com/watch?v=lyZQPjUT5B4

@ C C Z O N A
T H E E T H I C S &
R E S P O N S I B I L I T I E S O F
O P E N S O U R C E I O T
https://www.youtube.com/
watch?v=7rO5knyjDR0
Emily Gorcenski
@emilygorcenski

@ C C Z O N A
I M A G E U N D E R S TA N D I N G :  
D E E P L E A R N I N G W I T H
C O N V O L U T I O N A L N E U R A L N E T S
http://www.slideshare.net/
roelofp/python-for-image-
understanding-deep-learning-
with-convolutional-neural-nets
Roelof Pieters
@graphiﬁc

@ C C Z O N A
T H E E T H I C S O F B E I N G
A P R O G R A M M E R
https://youtu.be/
DB7ei5W1eRQ
Kate Heddleston
@heddle317

@ C C Z O N A
A H I P P O C R AT I C O AT H
F O R D ATA S C I E N C E
http://www.slideshare.net/
roelofp/a-hippocratic-oath-for-
data-science
Roelof Pieters
@graphiﬁc

@ C C Z O N A
D E E P L E A R N I N G :
I N T E L L I G E N C E F R O M  
B I G D ATA
https://www.youtube.com/
watch?v=czLI3oLDe8M
Adam Berenzweig
@madadam

@ C C Z O N A
A L G O R I T H M I C
A C C O U N TA B I L I T Y
W O R K S H O P
https://vimeo.com/125622175
Columbia Journalism School

@ C C Z O N A
M A R I / O
https://youtu.be/
qv6UVOQ0F44
Seth Bling
@sethbling

@ C C Z O N A
Noah Kantrowitz
Heidi Waterhouse
VM Brasseur
Yoz Grahame
Mike Foley 
Estelle Weyl
Chris Hausler
Scott Triglia
Russell Keith-Magee
Tim Bell
We So Crafty
THANK YOU ♥

@ C C Z O N A
BONUS!
“Eleven”
by Burnistoun (Robert Florence & Iain Connell)
https://www.youtube.com/watch?v=NMS2VnDveP8

Consequences of an Insightful Algorithm

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