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Escaping the Cult of Prediction (StrataConf NYC 2019)

Escaping the Cult of Prediction (StrataConf NYC 2019)

We’re living in a cultural moment is obsessed with making predictions. In politics and in business, we’re constantly coming up with ways to collect more data for a singular purpose: to predict what will happen next.

This overwhelming desire for prescience shapes the way we design, measure, and understand everything from products and marketing to politics and movements. Good predictions demand both precision and accuracy. Farrah Bostic walks you through how, in the quest to get more and more granular about how people will behave in the future, in the hopes that we can anticipate or manipulate that behavior, businesses are often tempted to rely on emerging or untested technologies—and sometimes pseudoscience—to get the “data” that fuels those predictions.

While this moment seems to be particularly defined by prediction, the practice goes back to (at least) the first lie detectors and has come to encompass practices like hypnosis, technology like medical imaging, and encoded anthropological approaches like microexpressions. But the implications are worse than wasting money and time. Businesses and brands are sacrificing the opportunity to understand things deeply and are simultaneously creating social negative externalities, like normalizing surveillance and misinformation, undermining public trust and values, and dehumanizing the very people whose behavior we want to predict.

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The Difference Engine

September 25, 2019
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Transcript

  1. UNDERSTANDING 
 ESCAPING THE CULT OF PREDICTION 25 September 2019,

    StrataConf NYC #stratadata Farrah Bostic, The Difference Engine
  2. None
  3. OK, LET’S GET STARTED

  4. @farrahbostic Head of Strategy & Research, Founder, 
 The Difference

    Engine 20 year in advertising & market research JD, Benjamin N Cardozo School of Law BA, Journalism & Communications, 
 University of Oregon
  5. 1992: 
 MR. MORTON’S ADVANCED ENGLISH ALL* YOU HAVE TO

    DO IN LIFE 
 IS MAKE CHOICES. *AND DIE, EVENTUALLY.
  6. 1995: 
 PS 492 DECISION MAKING 
 WITH PROF. JOHN

    ORBELL “[Hamlet’s] problem is making a judgment 
 about the facts surrounding a choice he must make, 
 doing so when some judgment cannot be avoided, 
 when the consequences of error are unthinkable, 
 and when the odds one way or the other are unknown.”
  7. 1999: 
 BELLAGIO HOTEL & CASINO “I follow the system.

    
 Sometimes I win, sometimes I lose. 
 I leave half my winnings in the casino bank. 
 That’s how I started my business.”
  8. 2002: 
 FIRST YEAR OF LAW SCHOOL 1066, the Norman

    Invasion, 
 “the King’s Peace” & the Rule of Law
  9. 2006: 
 MEETING WITH MY VODKA CLIENT “Which one of

    these campaigns will actually work?”
  10. The Difference Engine

  11. HOW DO 
 WE MAKE DECISIONS?

  12. IT’D BE NICE IF DECISION-MAKING WAS THIS EASY Profit!

  13. THERE ARE TWO MAIN KINDS OF DECISION-MAKING Outcome-oriented decisions: 


    Good outcomes = 
 good decisions Process-oriented decisions:
 Good processes = 
 good decisions
  14. FOR EXAMPLE, THE RULE OF LAW Formal characteristics: generality, publicity,

    prospectivity, intelligibility, consistency, practicability, congruence, stability Procedural characteristics: impartial & independent tribunal, right of representation, right to be present & participate in your defense, right to know why a judgment was made Values: a bond of reciprocity and a mutuality of constraint between ruler & ruled, predictability & reliability, liberty, dignity
  15. BUT OUTCOMES ARE WHAT MATTERS, RIGHT? If the focus of

    your decision-making is achievement - getting a particular outcome - then you have a lot riding on your ability to predict whether that outcome will occur. We should probably think about this probabilistically. But we’re not very good at that. 0% 100% v.
  16. Known Knowns Known Unknowns Unknown Knowns Unknown Unknowns

  17. MAKING PREDICTIONS INSTEAD OF MAKING DECISIONS

  18. HOW DO WE MAKE PREDICTIONS? If the prediction comes true,

    it’s a good prediction.
  19. PREDICTIONS DON’T LIVE IN A VACUUM PEOPLE TEND TO ACT

    IN RELIANCE ON THEM.
  20. SOME FOLKS HAVE TO MAKE PREDICTIONS, THOUGH. Being reliably precise

    & accurate is the key to credibility.
  21. PREDICTIONS = HYPOTHESES? NOPE. A GOOD PREDICTION IS RELIABLE, ACCURATE

    & PRECISE. A GOOD HYPOTHESIS IS DISPROVABLE.
  22. HOW DO WE FORM HYPOTHESES? Hypotheses are explanations of observed

    phenomena that can be disproved.
  23. HYPOTHESES > PREDICTIONS Outcome-oriented decisions: 
 Good outcomes = 


    good decisions Process-oriented decisions:
 Good processes = 
 good decisions PREDICTIONS HYPOTHESES
  24. PREDICTIONS TRAFFIC IN CERTAINTY. PEOPLE WOULD RATHER BE 
 CERTAINLY

    WRONG 
 THAN A LITTLE UNCERTAIN.
  25. USING ANALYTICS 
 AS A DRUNK 
 USES A LAMP

    POST FOR SUPPORT, RATHER THAN ILLUMINATION.
  26. PEOPLE LIKE 
 MAKING PREDICTIONS 
 MORE THAN THEY LIKE

    
 MAKING DECISIONS
  27. MAKING DECISIONS IS HARD AND HIGH STAKES. YOU’RE NOT EXPLAINING

    OR PREDICTING ANYMORE. YOU’RE CHOOSING. SO NOW IT'S ON YOU.
  28. SATISFICING & RESULTING We have “decision defaults” - the choice

    that’s good enough, what we’d do if we had no additional information, and what we'd do if we had quite a bit (but not “enough”) new information. Cassie Kozyrkov Chief Decision Scientist, 
 Google We equate the quality of a decision with the quality of its outcome, and succumb to the temptation to change our strategy just because it didn’t pay off immediately. Annie Duke Poker Player & 
 Decision Strategist
  29. WE ATTRIBUTE GOOD OUTCOMES TO GOOD DECISIONS. AND BAD OUTCOMES

    TO 
 “POOR RISK MANAGEMENT”. AND WE “MANAGE RISK” BY COLLECTING DATA, TO SUBSTITUTE PREDICTION FOR PROCESS, CERTAINTY FOR STRATEGY.
  30. THIS IS MOTIVATED REASONING. WE PAY ATTENTION TO CONFIRMING EVIDENCE

    AND DISCREDIT DISCONFIRMING EVIDENCE.
  31. BY THE WAY, PEOPLE TEND TO BELIEVE THAT QUALITATIVE SOURCES

    PRESENT A MUCH GREATER RISK OF PRODUCING DISCONFIRMING EVIDENCE. BUT THEY DON’T.
  32. WE ALSO THINK 
 PEOPLE ARE LIARS. WE’RE NOT, USUALLY.

  33. IF YOU GENUINELY BELIEVE THAT MOST PEOPLE ARE LIARS, WELL,

    FIRST OF ALL… IS EVERYTHING OKAY?
  34. SECOND, 
 ‘SHALLOW’ PSYCHOLOGY TELLS US: “THERE IS SOME GOOD

    REASON FOR MOST THINGS PEOPLE DO”
  35. SOME PROBLEMS CAN BE SO HARD THAT ANYONE WILL BE

    DRIVEN TO DISTRACTION - IT’S NOT THEIR PSYCHE, IT’S THE SITUATION.
  36. THIRD, [AHEM] WHAT MAKES YOU SO SPECIAL?

  37. “ A decision maker confronts risk when he or she

    can attach probabilities to alternative states of the world with confidence… Under uncertainty, not only can one still lose but one does not know the odds. - Professor John Orbell, 
 University of Oregon
  38. IN THIS ERA, WE COPE WITH UNCERTAINTY BY DEMANDING MORE

    DATA. IT’S BECOMING AN ADDICTION.
  39. OOH! MYSTICISM…

  40. DATA ADDICTION 
 LEADS TO 
 PROBLEMATIC DATA COLLECTION

  41. TECH & SCIENCE HAVE BEEN USED TO SCAM PREDICTION ADDICTS

    FOR DECADES
  42. DATA CAN BE 
 FRAUDULENT. DATA CAN BE 
 UNETHICAL.

  43. ANYBODY USED THESE TOOLS? ➤ Lie detectors ➤ Body language

    experts ➤ Hypnotists ➤ EEG ➤ fMRI ➤ Hidden cameras ➤ Sensors ➤ Computer vision ➤ Sentiment analysis
  44. THE FOUR HORSEMEN STALKERS LIE DETECTORS MIND READERS MENTALISTS

  45. STALKERS

  46. STALKERS AREN'T JUST FOLLOWING YOU, THEY'RE JUDGING & MANIPULATING YOU

  47. LIE DETECTORS

  48. MIND-READERS

  49. MENTALISTS

  50. SO WHAT DO WE DO?

  51. HAVE YOU TRIED ENGAGING WITH HUMANS?

  52. COLLECT DATA ETHICALLY

  53. INTERROGATE SOURCES AND METHODS ➤ Is the technology peer reviewed?

    ➤ Has the vendor published its results? ➤ Are the results replicable? ➤ Is the method in keeping with your values?
  54. INTERROGATE THE NATURE OF THE DATA ➤ What do they

    claim the data will tell you? 
 Reducing uncertainty is great, removing risk is a lie. ➤ Do people understand that this data is being collected and did they opt in? 
 Would you opt in if it were collecting on you? ➤ Who else wants this data, and why?
 Are you comfortable with that? ➤ What are the unintended consequences of collecting this data?
 Who could get hurt?
  55. MAKE DECISIONS INTENTIONALLY

  56. what do you w ant to do? what’s the desired

    outcome? what are your constraints? What are your v alues? what are your constraints? What is a good process? what dat a is necessary? act. identify options. what sources are best? what will you do with it? strategy process dat a decide
  57. THANK YOU! LET’S KEEP IN TOUCH. @FARRAHBOSTIC FARRAH@THEDIFFERENCEENGINE.CO