Consequences of an Insightful Algorithm

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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: http://storify.com/cczona/consequences-of-an-insightful-algorithm

KEYNOTE: RubyConf, JSConfEU, PyConAU, GOTO Berlin, Lean Agile Scotland, CUSEC, Open Source Bridge

ADDITIONAL: ArrrrCamp, EuRuKo, DjangoCon, WDCNZ, SCNA

Consequences of an Insightful Algorithm

  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
  5. @ C C Z O N A
  6. @ 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
  7. @ 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
  8. @ C C Z O N A
  9. @ C C Z O N A
  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
  18. @ C C Z O N A
  19. @ C C Z O N A
  20. @ C C Z O N A #DataMiningFail
  21. 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
  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? ”
  24. @ C C Z O N A
  25. @ C C Z O N A
  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
  28. @ C C Z O N A
  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."
  33. 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 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
  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
  35. @ C C Z O N A
  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
  38. @ C C Z O N A
  39. @ C C Z O N A
  40. @ C C Z O N A
  41. @ C C Z O N A
  42. @ C C Z O N A
  43. @ C C Z O N A
  44. 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 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
  45. @ C C Z O N A
  46. @ C C Z O N A
  47. 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 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
  48. @ C C Z O N A
  49. @ C C Z O N A
  50. @ C C Z O N A
  51. @ C C Z O N A
  52. @ C C Z O N A
  53. 🔵 🔵 🔵 🔵 🔵 🔵🔵 🔵 🔵 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
  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
  56. @ C C Z O N A G O O G L E A D W O R D S
  57. 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵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
  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
  67. @ C C Z O N A S H I R L E Y C A R D S
  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
  69. 🔵 🔵🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 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
  70. @ 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
  71. @ C C Z O N A
  72. @ 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
  73. @ 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
  74. @ 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
  75. @ C C Z O N A
  76. @ C C Z O N A
  77. @ C C Z O N A
  78. @ C C Z O N A UNDERLYING ASSUMPTIONS INFLUENCE OUTCOMES AND CONSEQUENCES @ C C Z O N A
  79. @ 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
  80. @ C C Z O N A OUR INDUSTRY IS 
 IN AN ARMS RACE
  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
  83. @ C C Z O N A FLIPPING THE PARADIGM
  84. @ 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
  85. @ 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
  86. @ C C Z O N A FIRST DO NO HARM
  87. @ 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
  88. @ 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
  89. @ 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
  90. @ C C Z O N A
  91. @ 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
  92. @ 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
  93. @ 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
  94. @ C C Z O N A The point of culture fit is to avoid disruption of groupthink
  95. @ C C Z O N A Unidimensional variety
 is not diversity
  96. @ C C Z O N A
  97. @ 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
  98. @ C C Z O N A
  99. @ C C Z O N A AUDIT OUTCOMES CONSTANTLY
  100. @ C C Z O N A AUDIT OUTCOMES CONSTANTLY
  101. @ C C Z O N A AUDIT OUTCOMES
  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
  104. @ C C Z O N A Party.
  105. @ C C Z O N A
  106. @ C C Z O N A
  107. @ C C Z O N A
  108. @ C C Z O N A
  109. @ C C Z O N A
  110. @ C C Z O N A
  111. @ C C Z O N A
  112. @ 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
  113. @ C C Z O N A
  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
  119. @ C C Z O N A
  120. @ C C Z O N A We DO NOT BUILD here 
 without understanding CONSEQUENCES TO OTHERS
  121. @ C C Z O N A PROFESSIONALS
 APPLY EXPERTISE & JUDGEMENT ABOUT 
 HOW TO 
 SOLVE PROBLEMS
  122. 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 ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌ ❌❌ ❌ ❌ ❌ ❌❌ ❌ ❌ ❌ ❌ ❌ ❌
  123. @ C C Z O N A R E F U S E T O 
 P L AY A L O N G .
  124. @ C C Z O N A T H A N K Y O U .
  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
  141. @ C C Z O N A

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