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@ 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
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infertility
sex shaming
miscarriage
bullying
stalking
grief
PTSD
segregation
racial profiling
white supremacy
holocaust
CONTENT WARNING
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ALGORITHMS IMPOSE CONSEQUENCES ON PEOPLE ALL THE TIME
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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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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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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 R Y L I F E
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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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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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ALGORITHMS FOR FAST, TRAINABLE , ARTIFICIAL NEURAL NETWORKS
D E E P L E A R N I N G
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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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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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– P e t e Wa r d 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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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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Ad Targeting Behavioral Prediction Recommendation Systems Image Classification Face Recognition
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#DataMiningFail
Algorithmic Profiling
De-Anonymization Black Box Disparate
ImpactConsentIssues
ReplicatesBias
Personally Identifiable Info
Human Complexity Fail
Unproven Methods
Inadvertent Algorithmic
CrueltyUncritical
AssumptionsFalse
NeutralityHigh Risk
Consequences Deception Filtering
Moved Fast, Broke Things
Invasion Of Privacy No Recourse Creepy
StalkeryMessing With
Heads
Not How Valid Research Works
DataInsecurity
Accurate, But Not Right Shaming Diversity
Fail
#DataMiningFail
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🔵Algorithmic Profiling
De-Anonymization Black Box Disparate
ImpactConsentIssues
ReplicatesBias
Personally Identifiable Info
Human Complexity Fail
Unproven Methods
Inadvertent Algorithmic
CrueltyUncritical
AssumptionsFalse
NeutralityHigh Risk
Consequences Deception Filtering
Moved Fast, Broke Things
Invasion Of Privacy No Recourse Creepy
StalkeryMessing With
Heads
Not How Valid Research Works
DataInsecurity
Accurate, But Not Right Shaming Diversity
Fail
TA R G E T
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“IF WE WANTED TO FIGURE OUTIF A CUSTOMER IS PREGNANT,
EVEN IF SHE DIDN’T WANT US TO KNOW,
CAN YOU DO THAT? ”
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‟As long as a pregnant woman thinks she hasn’t been spied on… as long as we don’t spook her, it works.”
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Algorithmic Profiling
De-Anonymization Black Box Disparate
ImpactConsentIssues
ReplicatesBias
Personally Identifiable Info
Human Complexity Fail
Unproven Methods
Inadvertent Algorithmic
CrueltyUncritical
AssumptionsFalse
NeutralityHigh Risk
Consequences Deception Filtering
Moved Fast, Broke Things
Invasion Of Privacy No Recourse Creepy
StalkeryMessing With
Heads
Not How Valid Research Works
DataInsecurity
Accurate, But Not Right Shaming Diversity
Fail
S H U T T E R F LY
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‟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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‟ 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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‟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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‟You start imagining who they'll become and dreaming of hopes for their future. You start making plans. And then they're gone.
It's a lonely experience."
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Algorithmic Profiling
De-Anonymization Black Box Disparate
ImpactConsentIssues
ReplicatesBias
Personally Identifiable Info
Human Complexity Fail
Unproven Methods
Inadvertent Algorithmic
CrueltyUncritical
AssumptionsFalse
NeutralityHigh Risk
Consequences Deception Filtering
Moved Fast, Broke Things
Invasion Of Privacy No Recourse Creepy
StalkeryMessing With
Heads
Not How Valid Research Works
DataInsecurity
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
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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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‟ I N C R E A S E A W A 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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BE HUMBLE. WE CANNOT INTUIT INNER STATE, EMOTIONS, PRIVATE SUBJECTIVITY
— C a r i n a C . Z o n a
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Algorithmic Profiling
De-Anonymization Black Box Disparate
ImpactConsentIssues
ReplicatesBias
Personally Identifiable Info
Human Complexity Fail
Unproven Methods
Inadvertent Algorithmic
CrueltyUncritical
AssumptionsFalse
NeutralityHigh Risk
Consequences Deception Filtering
Moved Fast, Broke Things
Invasion Of Privacy No Recourse Creepy
StalkeryMessing With
Heads
Not How Valid Research Works
DataInsecurity
Accurate, But Not Right Shaming Diversity
Fail
F I T B I T
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@ C C Z O N A
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Algorithmic Profiling
De-Anonymization Black Box Disparate
ImpactConsentIssues
ReplicatesBias
Personally Identifiable Info
Human Complexity Fail
Unproven Methods
Inadvertent Algorithmic
CrueltyUncritical
AssumptionsFalse
NeutralityHigh Risk
Consequences Deception Filtering
Moved Fast, Broke Things
Invasion Of Privacy No Recourse Creepy
StalkeryMessing With
Heads
Not How Valid Research Works
DataInsecurity
Accurate, But Not Right Shaming Diversity
Fail
U B E R
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🔵Algorithmic Profiling
De-Anonymization Black Box Disparate
ImpactConsentIssues
ReplicatesBias
Personally Identifiable Info
Human Complexity Fail
Unproven Methods
Inadvertent Algorithmic
CrueltyUncritical
AssumptionsFalse
NeutralityHigh Risk
Consequences Deception Filtering
Moved Fast, Broke Things
Invasion Of Privacy No Recourse Creepy
StalkeryMessing With
Heads
Not How Valid Research Works
DataInsecurity
Accurate, But Not Right Shaming Diversity
Fail
G O O G L E A D W O R D S
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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 LIKELYTO 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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G O O G L E A D W O R D S
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🔵Algorithmic Profiling
De-Anonymization Black Box Disparate
ImpactConsentIssues
ReplicatesBias
Personally Identifiable Info
Human Complexity Fail
Unproven Methods
Inadvertent Algorithmic
CrueltyUncritical
AssumptionsFalse
NeutralityHigh Risk
Consequences Deception Filtering
Moved Fast, Broke Things
Invasion Of Privacy No Recourse Creepy
StalkeryMessing With
Heads
Not How Valid Research Works
DataInsecurity
Accurate, But Not Right Shaming Diversity
Fail
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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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A L G O R I T H M I C H U B R I S
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"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
ImpactConsentIssues
ReplicatesBias
Personally Identifiable Info
Human Complexity Fail
Unproven Methods
Inadvertent Algorithmic
CrueltyUncritical
AssumptionsFalse
NeutralityHigh Risk
Consequences Deception Filtering
Moved Fast, Broke Things
Invasion Of Privacy No Recourse Creepy
StalkeryMessing With
Heads
Not How Valid Research Works
DataInsecurity
Accurate, But Not Right Shaming Diversity
Fail
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I P H O T O FA C E D E T E C T I O N
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M I C R O S O F T H O W - O L D . N E T
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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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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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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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S H I R L E Y C A R D S
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The tools used to make film,
b y M o n t r é A z a M i s s o u r i
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notare the science of it,
raciallyneutral
S H I R L E Y C A R D S
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Algorithmic Profiling
De-Anonymization Black Box Disparate
ImpactConsentIssues
ReplicatesBias
Personally Identifiable Info
Human Complexity Fail
Unproven Methods
Inadvertent Algorithmic
CrueltyUncritical
AssumptionsFalse
NeutralityHigh Risk
Consequences Deception Filtering
Moved Fast, Broke Things
Invasion Of Privacy No Recourse Creepy
StalkeryMessing With
Heads
Not How Valid Research Works
DataInsecurity
Accurate, But Not Right Shaming Diversity
Fail
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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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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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!=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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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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UNDERLYING ASSUMPTIONS INFLUENCE OUTCOMES AND CONSEQUENCES
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— H e n r y D a v i d T h o r e 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 ”
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OUR INDUSTRY IS IN AN ARMS RACE
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Amazon
Apple
AT&T
Baidu
DARPA
Dropbox
eBay
Flickr
IBM
Microsoft
Netflix
Pandora
PayPal
Skype
Snapchat
Spotify
Tesla
Yahoo
Uber
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MORE PRECISEIN CORRECTNESS MORE
DAMAGING IN WRONGNESS
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FLIPPING THE PARADIGM
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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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•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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FIRST DO NO HARM
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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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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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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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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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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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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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The point of culture fit is to avoid disruption of groupthink
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Unidimensional variety is not diversity
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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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AUDIT OUTCOMES CONSTANTLY
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AUDIT OUTCOMES CONSTANTLY
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AUDIT OUTCOMES
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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 R TA 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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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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Party.
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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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"LIST THINGS YOU DON'T WANT TO THINK ABOUT"
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Opt-Out Opt-InBlock FollowIgnore ViewFilter 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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"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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Would you like special coupons for pregnancy & infant care?
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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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We DO NOT BUILD here without understanding CONSEQUENCES TO OTHERS
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PROFESSIONALS APPLY EXPERTISE & JUDGEMENT
ABOUT
HOW TO SOLVE PROBLEMS
Algorithmic Profiling
De-Anonymization Black Box Disparate
ImpactConsentIssues
ReplicatesBias
Personally Identifiable Info
Human Complexity Fail
Unproven Methods
Inadvertent Algorithmic
CrueltyUncritical
AssumptionsFalse
NeutralityHigh Risk
Consequences Deception Filtering
Moved Fast, Broke Things
Invasion Of Privacy No Recourse Creepy
StalkeryMessing With
Heads
Not How Valid Research Works
DataInsecurity
Accurate, But Not Right Shaming Diversity
Fail
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R E F U S E T O P L AY A L O N G .
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T H A N K Y O U .
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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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CASE STUDIESFitBit: 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
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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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FLICKR CREATIVE COMMONSCookie 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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NOUN PROJECT CREATIVE COMMONSBusinesspeople 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 Stoianhttps://thenounproject.com/term/credit-card/269411/
Single House by Aaron K. Kimhttps://thenounproject.com/term/single-house/123924/
Downloads by Icons8 https://thenounproject.com/term/downloads/61761/
Id Card (1) by Boudewijn Mijnlieffhttps://thenounproject.com/term/id-card/203566/
Id Card (2) by Boudewijn Mijnlieffhttps://thenounproject.com/term/id-card/203578/
Cancel by Kris Brauerhttps://thenounproject.com/term/cancel/182501/
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ADDITIONAL IMAGESRecipe 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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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
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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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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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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
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