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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

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

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N A ALGORITHMS IMPOSE CONSEQUENCES ON PEOPLE ALL THE TIME

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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

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

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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

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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.

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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

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

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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

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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

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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 . "

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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

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N A Ad Targeting Behavioral Prediction Recommendation Systems Image Classification Face Recognition

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N A #DataMiningFail

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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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? ”

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N A

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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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N A

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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...”

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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

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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 "

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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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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

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N A

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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

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

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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

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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

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N A G O O G L E A D W O 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

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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

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

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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

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

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

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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

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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

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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

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N A S H I R L E Y C A R D S

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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

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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

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N A

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

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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

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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

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N A

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

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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

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N A OUR INDUSTRY IS IN AN ARMS RACE

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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

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N A MOREPRECISE INCORRECTNESS MORE DAMAGING INWRONGNESS

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N A FLIPPING THE PARADIGM

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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

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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

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N A FIRST DO NO HARM

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N A BE HONEST BE TRUSTWORTHY 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 2

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N A BUILD IN RECOURSE 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

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N A FULLY DISCLOSE LIMITATIONS. CALL ATTENTION TO SIGNS OF RISK OF HARM. F L I P P I N G T H E PA R A D I G M 3

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N A

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N A BE VISIONARY ABOUT COUNTERING BIAS F L I P P I N G T H E PA R A D I G M 4

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

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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

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N A The point of culture fit is to avoid disruption of groupthink

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N A Unidimensional variety is not diversity

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N A

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N A AUDIT OUTCOMES CONSTANTLY F L I P P I N G T H E PA R A D I G M 6

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N A

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N A AUDIT OUTCOMES CONSTANTLY

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N A AUDIT OUTCOMES CONSTANTLY

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N A AUDIT OUTCOMES

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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

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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

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N A Party.

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N A CULTIVATE INFORMED CONSENT F L I P P I N G T H E PA R A D I G M 8

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N A "LIST THINGS YOU DON'T WANT TO THINK ABOUT"

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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

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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

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N A Would you like special coupons for pregnancy & infant care?

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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

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N A We DO NOT BUILD here without understanding CONSEQUENCES TO OTHERS

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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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N A R E F U S E T O P L AY A L O N G .

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N A T H A N K Y O U .

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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

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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

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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

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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

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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/

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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/

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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

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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

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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

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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

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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

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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

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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

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

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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 ♥

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

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