We have ethical responsibilities when coding. We’re able to extract remarkably precise intuitions about an individual. But do we have a right to know what they didn’t consent to share, even when they willingly shared the data that leads us there? A major retailer’s data-driven marketing accidentially revealed to a teen’s family that she was pregnant. Eek.
What are our obligations to people who did not expect themselves to be so intimately known without sharing directly? How do we mitigate against unintended outcomes? For instance, an activity tracker carelessly revealed users’ sexual activity data to search engines. A social network’s algorithm accidentally triggered painful memories for grieving families who’d recently experienced death of their child and other loved ones.
We design software for humans. Balancing human needs and business specs can be tough. It’s crucial that we learn how to build in systematic empathy.
In this talk, we'll delve into specific examples of uncritical programming, and painful results from using insightful data in ways that were benignly intended. You’ll learn ways we can integrate practices for examining how our code might harm individuals. We’ll look at how to flip the paradigm, netting consequences that can be better for everyone.
SLIDES: http://www.slideshare.net/cczona/consequences-of-an-insightful-algorithm
VIDEO: http://confreaks.tv/videos/rubyconf2015-keynote-consequences-of-an-insightful-algorithm
REVIEWS: https://wakelet.com/wake/5758ef98-8e71-4854-9ea2-683e0b5c98a3
KEYNOTE: RubyConf, JSConfEU, PyConAU, GOTO Berlin, Lean Agile Scotland, CUSEC, Open Source Bridge
ADDITIONAL: ArrrrCamp, EuRuKo, DjangoCon, WDCNZ, SCNA
1. @ 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
2. @ C C Z O N A
infertility
sex shaming
miscarriage
bullying
stalking
grief
PTSD
segregation
racial profiling
white supremacy
holocaust
CONTENT WARNING
3. @ C C Z O N A
ALGORITHMS
IMPOSE
CONSEQUENCES
ON PEOPLE
ALL THE TIME
4. @ 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
10. @ 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.
11. @ 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
12. @ 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
13. @ 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
14. @ 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
15. @ 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 . "
16. @ 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
17. @ C C Z O N A
Ad Targeting
Behavioral Prediction
Recommendation Systems
Image Classification
Face Recognition
22. 🔵 🔵
🔵
🔵
🔵
🔵
🔵 🔵
🔵🔵
🔵
🔵
🔵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
23. @ 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? ”
26. @ 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.”
27. 🔵 🔵 🔵 🔵
🔵
🔵
🔵 🔵
🔵
🔵
🔵
🔵
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
29. @ 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...”
30. @ 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
31. @ 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 "
32. @ 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."
34. @ 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
36. @ 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
37. @ C C Z O N A
BEHUMBLE.WECANNOTINTUIT
INNERSTATE,EMOTIONS,
PRIVATESUBJECTIVITY
— C a r i n a C . Z o n a
54. @ 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
55. 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
G O O G L E A D W O R D S
58. @ 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
59. @ C C Z O N A
A L G O R I T H M I C H U B R I S
60. @ 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
61. 🔵🔵
🔵
🔵
🔵
🔵
🔵 🔵
🔵 🔵
🔵
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
62. @ C C Z O N A
I P H O T O FA C E D E T E C T I O N
63. @ C C Z O N A
M I C R O S O F T H O W - O L D . N E T
64. @ 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
65. @ 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
66. @ 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
68. @ 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
81. @ 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
82. @ C C Z O N A
MOREPRECISE
INCORRECTNESS
MORE
DAMAGING
INWRONGNESS
102. @ 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
103. @ 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
F L I P P I N G T H E PA R A D I G M
114. @ C C Z O N A
"LIST THINGS YOU
DON'T WANT
TO THINK ABOUT"
115. @ 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
116. @ 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
117. @ C C Z O N A
Would you like
special coupons for
pregnancy & infant
care?
118. @ C C Z O N A
COMMIT TO DATA
TRANSPARENCY.
COMMIT TO ALGORITHMIC
TRANSPARENCY.
F L I P P I N G T H E PA R A D I G M
9
125. @ 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
126. @ 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
127. @ 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
128. @ 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
129. @ 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/
130. @ 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/
131. @ 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
132. @ 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
133. @ 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
134. @ 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
135. @ 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
136. @ 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
137. @ 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
138. @ C C Z O N A
M A R I / O
https://youtu.be/
qv6UVOQ0F44
Seth Bling
@sethbling
139. @ 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 ♥
140. @ C C Z O N A
BONUS!
“Eleven”
by Burnistoun (Robert Florence & Iain Connell)
https://www.youtube.com/watch?v=NMS2VnDveP8