Skip to main content
UploadLanguage (EN)Support
BusinessMobileSocial MediaMarketingTechnologyArt & PhotosCareerDesignEducationPresentations & Public SpeakingGovernment & NonprofitHealthcareInternetLawLeadership & ManagementAutomotiveEngineeringSoftwareRecruiting & HRRetailSalesServicesScienceSmall Business & EntrepreneurshipFoodEnvironmentEconomy & FinanceData & AnalyticsInvestor RelationsSportsSpiritualNews & PoliticsTravelSelf ImprovementReal EstateEntertainment & HumorHealth & MedicineDevices & HardwareLifestyle
Carina C. Zona, profile picture
Uploaded byCarina C. Zona
87,296 views

Consequences of an Insightful Algorithm

The document discusses the implications of algorithms on society, highlighting their potential consequences such as algorithmic profiling, bias replication, and privacy invasions. It critiques the reliance on automated systems, emphasizing instances of inadvertent algorithmic cruelty and the failures in understanding human complexity. Additionally, it raises concerns about the ethical responsibilities of technology companies and the need for greater awareness of the impact their algorithms have on individuals.

Data & Analytics◦Internet◦Software◦
Related topics:
Data Science Insights• Deep Learning•Data Mining Insights• Social Networking•
Downloaded 180 times
@ 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
@ C C Z O N A
@ 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
@ C C Z O N A
@ C C Z O N A
@ 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
@ C C Z O N A
@ C C Z O N A
@ 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? ”
@ C C Z O N A
@ C C Z O N A
@ 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
@ C C Z O N A
@ 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
@ C C Z O N A
@ 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
@ C C Z O N A
@ C C Z O N A
@ C C Z O N A
@ C C Z O N A
@ C C Z O N A
@ C C Z O N A
🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 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
@ C C Z O N A
@ C C Z O N A
🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 🔵 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
@ C C Z O N A
@ C C Z O N A
@ C C Z O N A
@ C C Z O N A
@ C C Z O N A
🔵 🔵 🔵 🔵 🔵 🔵🔵 🔵 🔵 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
@ C C Z O N A A L G O R I T H M I C H U B R I S
@ 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
@ C C Z O N A
@ 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
@ C C Z O N A
@ C C Z O N A
@ C C Z O N A
@ 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
@ C C Z O N A
@ 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
@ C C Z O N A
@ 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
@ C C Z O N A
@ C C Z O N A AUDIT OUTCOMES CONSTANTLY
@ C C Z O N A AUDIT OUTCOMES CONSTANTLY