Consequences of an Insightful Algorithm

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
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@ 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
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@ 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
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@ C C Z O 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
🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵🔵 🔵 🔵 🔵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? ”
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@ 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.”
🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 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
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@ 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."
🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 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
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@ 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 G O O G L E A D W O R D S
@ C C Z O N A G O O G L E A D W O R D S
🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵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
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@ 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
🔵🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 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 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
🔵 🔵🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 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
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@ 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
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@ 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 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
@ C C Z O 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
@ C C Z O 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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@ C C Z O 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
@ 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
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@ C C Z O 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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@ C C Z O N A AUDIT OUTCOMES CONSTANTLY
@ C C Z O N A AUDIT OUTCOMES CONSTANTLY
@ C C Z O N A AUDIT OUTCOMES
@ 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 F L I P P I N G T H E PA R A D I G M
@ C C Z O N A Party.
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@ C C Z O 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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@ 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. F L I P P I N G T H E PA R A D I G M 9
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@ 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 ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌❌ ❌ ❌ ❌ ❌❌ ❌ ❌ ❌ ❌ ❌ ❌
@ 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
@ C C Z O N A

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