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RubyConf_2017_-_Kelsey_Pedersen.pdf

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November 17, 2017
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 RubyConf_2017_-_Kelsey_Pedersen.pdf

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Kelsey Pedersen

November 17, 2017
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Transcript

  1. HUMAN DECISION MAKING Kelsey Pedersen Software Engineer, Stitch Fix with

    DATA SCIENCE AUGMENTING @kelsey_pedersen

  2. @kelsey_pedersen KELSEY PEDERSEN

  3. I’m a software engineer at Stitch Fix - a personalized

    styling service
  4. None
  5. None
  6. None
  7. None
  8. None
  9. None

  10. stylists data scientists

  11. None
  12. None

  13. How do humans make decisions? What are the limitations of

    human decision making? How can we help users make decisions with data science?

  14. How do humans make decisions? Human Decisions Limits of Decisions

    Stylists Decisions Humans & Data Science The Future

  15. System 1 System 2

  16. System 1 impressions & feelings

  17. None
  18. None
  19. None
  20. None

  21. 95% of human decisions are made instantaneously

  22. System 2 analytical & effortful

  23. 1737 x 1990 Answer: 3,456,630

  24. None
  25. None

  26. System 1 gut feelings System 2 computations

  27. LEFT BRAIN RIGHT BRAIN

  28. What are the limits in human decision making? Human Decisions

    Limits of Decisions Stylists Decisions Humans & Data Science The Future
  29. None

  30. Human decisions are unpredictable

  31. Inconsistent Judgments

  32. Inconsistent Judgments

  33. Inconsistent Judgments

  34. Human decisions are driven by your own past experiences

  35. Limited Information

  36. Limited Information

  37. Limited Information

  38. Human decisions are driven by your own views and preferences

  39. Biased Decisions

  40. Biased Decisions

  41. Biased Decisions

  42. RISKY & UNPREDICTABLE

  43. What are examples of Stitch Fix stylists making decisions? Human

    Decisions Limits of Decisions Stylists Decisions Humans & Data Science The Future

  44. Inconsistent Judgments by Stylists

  45. Inconsistent Judgments by Stylists

  46. Limited Information by Stylists

  47. Limited Information by Stylists

  48. Biased Decisions by Stylists

  49. None

  50. In what ways can data science augment human decisions? Human

    Decisions Limits of Decisions Stylists Decisions Humans & Data Science The Future
  51. None

  52. Data Science Noun: Use of mathematics or statistics to answer

    a business question

  53. GUIDE DECISIONS WITH COMPUTATIONS TRAIN DECISIONS WITH FEEDBACK

  54. DATA STYLING STYLING STYLING STYLING STYLING STYLING STYLING STYLING DATA

    DATA DATA DATA DATA
  55. None

  56. Deciding on one item

  57. None

  58. 72% 60% 48% 44%

  59. None
  60. None
  61. None

  62. Deciding on all 5 items

  63. 72% 60% 48% 44% 76%

  64. 72% 60% 48% 44% 24%

  65. None

  66. Client Feedback on all 5 items

  67. None
  68. None
  69. None

  70. Client Feedback overtime for one Stylist

  71. None
  72. None
  73. None

  74. All Feedback for all Stylists

  75. None
  76. None

  77. Can humans augment algorithms? Human Decisions Limits of Decisions Stylists

    Decisions Humans & Data Science The Future
  78. None
  79. None
  80. None
  81. None
  82. None

  83. 72% 60% 48% 10%

  84. 72% 60% 48% 44% 24%

  85. None
  86. None

  87. stylists data scientists

  88. What is the future of data science? Human Decisions Limits

    of Decisions Stylists Decisions Humans & Data Science The Future
  89. None

  90. LEFT BRAIN RIGHT BRAIN VS.

  91. System 1 gut feelings System 2 computations &

  92. None
  93. None

  94. Data Science Humans System 1 System 2 Data Science Humans

    System 3

  95. System 3 predictive & intuitive

  96. stylists data scientists

  97. TRAIN Performance Metrics

  98. None

  99. System 1 gut feelings System 2 computations System 3 feedback

  100. What is the business impact of system 3?

  101. lower labor cost fewer mistakes from humans greater client satisfaction

    increased keep rate

  102. In conclusion…

  103. Conclusion

  104. None
  105. None

  106. Conclusion

  107. humans data science

  108. @kelsey_pedersen KELSEY PEDERSEN