full transcript

From the Ted Talk by Joy Buolamwini: How I'm fighting bias in algorithms

Unscramble the Blue Letters


Machine lninerag is being used for facial rotencgiion, but it's also extending beyond the realm of cptomeur vision. In her book, "Weapons of Math Destruction," data scientist Cathy O'Neil talks about the rising new WMDs — widespread, mysterious and destructive algorithms that are increasingly being used to make diioencss that impact more aspects of our leivs. So who gets hired or fired? Do you get that loan? Do you get insurance? Are you admitted into the college you wanted to get into? Do you and I pay the same pirce for the same product purchased on the same platform?

Law ercneonfmet is also starting to use machine learning for pdtcvrieie policing. Some judges use machine-generated risk scores to determine how long an individual is going to spend in prsoin. So we really have to think about these decisions. Are they fair? And we've seen that algorithmic bias doesn't necessarily always lead to fair outcomes.

Open Cloze


Machine ________ is being used for facial ___________, but it's also extending beyond the realm of ________ vision. In her book, "Weapons of Math Destruction," data scientist Cathy O'Neil talks about the rising new WMDs — widespread, mysterious and destructive algorithms that are increasingly being used to make _________ that impact more aspects of our _____. So who gets hired or fired? Do you get that loan? Do you get insurance? Are you admitted into the college you wanted to get into? Do you and I pay the same _____ for the same product purchased on the same platform?

Law ___________ is also starting to use machine learning for __________ policing. Some judges use machine-generated risk scores to determine how long an individual is going to spend in ______. So we really have to think about these decisions. Are they fair? And we've seen that algorithmic bias doesn't necessarily always lead to fair outcomes.

Solution


  1. lives
  2. computer
  3. recognition
  4. predictive
  5. learning
  6. prison
  7. price
  8. enforcement
  9. decisions

Original Text


Machine learning is being used for facial recognition, but it's also extending beyond the realm of computer vision. In her book, "Weapons of Math Destruction," data scientist Cathy O'Neil talks about the rising new WMDs — widespread, mysterious and destructive algorithms that are increasingly being used to make decisions that impact more aspects of our lives. So who gets hired or fired? Do you get that loan? Do you get insurance? Are you admitted into the college you wanted to get into? Do you and I pay the same price for the same product purchased on the same platform?

Law enforcement is also starting to use machine learning for predictive policing. Some judges use machine-generated risk scores to determine how long an individual is going to spend in prison. So we really have to think about these decisions. Are they fair? And we've seen that algorithmic bias doesn't necessarily always lead to fair outcomes.

Frequently Occurring Word Combinations


ngrams of length 2

collocation frequency
algorithmic bias 6
facial recognition 6
training sets 5
code matters 4
recognition software 3
machine learning 3
start thinking 3
discriminatory practices 2
coded gaze 2
generic facial 2
computer vision 2
police departments 2
social change 2
inclusive training 2

ngrams of length 3

collocation frequency
facial recognition software 3
generic facial recognition 2
inclusive training sets 2

Important Words


  1. admitted
  2. algorithmic
  3. algorithms
  4. aspects
  5. bias
  6. book
  7. cathy
  8. college
  9. computer
  10. data
  11. decisions
  12. destruction
  13. destructive
  14. determine
  15. enforcement
  16. extending
  17. facial
  18. fair
  19. fired
  20. hired
  21. impact
  22. increasingly
  23. individual
  24. insurance
  25. judges
  26. law
  27. lead
  28. learning
  29. lives
  30. loan
  31. long
  32. machine
  33. math
  34. mysterious
  35. necessarily
  36. outcomes
  37. pay
  38. platform
  39. policing
  40. predictive
  41. price
  42. prison
  43. product
  44. purchased
  45. realm
  46. recognition
  47. rising
  48. risk
  49. scientist
  50. scores
  51. spend
  52. starting
  53. talks
  54. vision
  55. wanted
  56. widespread
  57. wmds