full transcript

From the Ted Talk by Thomas Hofweber: Can AI predict someone's breakup?

Unscramble the Blue Letters


The uncertainty undermining all these options stems from a well known isuse with AI around explainability and tparsnnceary. This problem plagues tons of potentially useful predictive models, such as those that could be used to predict which bank customers are most likely to rpaey a loan, or which prisoners are most likely to reoffend if granted prolae. Without knowing why AI systems reach their decisions, many worry we can’t think critically about how to follow their advice.

But the transparency problem doesn’t just prevent us from undtndrasnieg these mdeols, it also impacts the user’s acnoiabluttciy. For example, if the AI's prediction led you to break up with Alex, what explanation could you reasonably oeffr them? That you want to end your happy roastlnhieip because some mysterious machine predicted its dmiese? That hardly seems fair to Alex. We don’t always owe people an explanation for our actions, but when we do, AI’s lack of transparency can create ethically challenging suotniaits. And accountability is just one of the trdfaoefs we make by outsourcing important dioiesncs to AI. If you’re comfortable deferring your agency to an AI model it’s likely because you’re fsoceud on the aacucrcy of the prediction. In this mindset, it doesn’t really matter why you and Alex might break up— simply that you likely will. But if you prioritize authenticity over accuracy, then you'll need to understand and appreciate the reasons for your future divorce before ending things today. Authentic decision making like this is essential for maintaining accountability, and it might be your best chcane to prove the prediction wrong. On the other hand, it’s also possible the model already accounted for your attempts to defy it, and you’re just setting yourself up for failure.

Open Cloze


The uncertainty undermining all these options stems from a well known _____ with AI around explainability and ____________. This problem plagues tons of potentially useful predictive models, such as those that could be used to predict which bank customers are most likely to _____ a loan, or which prisoners are most likely to reoffend if granted ______. Without knowing why AI systems reach their decisions, many worry we can’t think critically about how to follow their advice.

But the transparency problem doesn’t just prevent us from _____________ these ______, it also impacts the user’s ______________. For example, if the AI's prediction led you to break up with Alex, what explanation could you reasonably _____ them? That you want to end your happy ____________ because some mysterious machine predicted its ______? That hardly seems fair to Alex. We don’t always owe people an explanation for our actions, but when we do, AI’s lack of transparency can create ethically challenging __________. And accountability is just one of the _________ we make by outsourcing important _________ to AI. If you’re comfortable deferring your agency to an AI model it’s likely because you’re _______ on the ________ of the prediction. In this mindset, it doesn’t really matter why you and Alex might break up— simply that you likely will. But if you prioritize authenticity over accuracy, then you'll need to understand and appreciate the reasons for your future divorce before ending things today. Authentic decision making like this is essential for maintaining accountability, and it might be your best ______ to prove the prediction wrong. On the other hand, it’s also possible the model already accounted for your attempts to defy it, and you’re just setting yourself up for failure.

Solution


  1. transparency
  2. tradeoffs
  3. accuracy
  4. demise
  5. repay
  6. issue
  7. decisions
  8. relationship
  9. models
  10. parole
  11. offer
  12. accountability
  13. chance
  14. understanding
  15. situations
  16. focused

Original Text


The uncertainty undermining all these options stems from a well known issue with AI around explainability and transparency. This problem plagues tons of potentially useful predictive models, such as those that could be used to predict which bank customers are most likely to repay a loan, or which prisoners are most likely to reoffend if granted parole. Without knowing why AI systems reach their decisions, many worry we can’t think critically about how to follow their advice.

But the transparency problem doesn’t just prevent us from understanding these models, it also impacts the user’s accountability. For example, if the AI's prediction led you to break up with Alex, what explanation could you reasonably offer them? That you want to end your happy relationship because some mysterious machine predicted its demise? That hardly seems fair to Alex. We don’t always owe people an explanation for our actions, but when we do, AI’s lack of transparency can create ethically challenging situations. And accountability is just one of the tradeoffs we make by outsourcing important decisions to AI. If you’re comfortable deferring your agency to an AI model it’s likely because you’re focused on the accuracy of the prediction. In this mindset, it doesn’t really matter why you and Alex might break up— simply that you likely will. But if you prioritize authenticity over accuracy, then you'll need to understand and appreciate the reasons for your future divorce before ending things today. Authentic decision making like this is essential for maintaining accountability, and it might be your best chance to prove the prediction wrong. On the other hand, it’s also possible the model already accounted for your attempts to defy it, and you’re just setting yourself up for failure.

Frequently Occurring Word Combinations


ngrams of length 2

collocation frequency
happy relationship 2


Important Words


  1. accountability
  2. accounted
  3. accuracy
  4. actions
  5. advice
  6. agency
  7. ai
  8. alex
  9. attempts
  10. authentic
  11. authenticity
  12. bank
  13. break
  14. challenging
  15. chance
  16. comfortable
  17. create
  18. critically
  19. customers
  20. decision
  21. decisions
  22. deferring
  23. defy
  24. demise
  25. divorce
  26. essential
  27. ethically
  28. explainability
  29. explanation
  30. failure
  31. fair
  32. focused
  33. follow
  34. future
  35. granted
  36. hand
  37. happy
  38. impacts
  39. important
  40. issue
  41. knowing
  42. lack
  43. led
  44. loan
  45. machine
  46. maintaining
  47. making
  48. matter
  49. mindset
  50. model
  51. models
  52. mysterious
  53. offer
  54. options
  55. outsourcing
  56. owe
  57. parole
  58. people
  59. plagues
  60. potentially
  61. predict
  62. predicted
  63. prediction
  64. predictive
  65. prevent
  66. prioritize
  67. prisoners
  68. problem
  69. prove
  70. reach
  71. reasons
  72. relationship
  73. reoffend
  74. repay
  75. setting
  76. simply
  77. situations
  78. stems
  79. systems
  80. today
  81. tons
  82. tradeoffs
  83. transparency
  84. uncertainty
  85. undermining
  86. understand
  87. understanding
  88. worry
  89. wrong