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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 182 · 40 questions
TalentJR

AI tip (Fair data, fairer AI): Bias usually comes from the training data: if a group is missing or shown in only one way, the model copies that. One-sided data: one age group, only ripe mangoes, only adult voices, only neat handwriting. Balanced data: many ages, colours, voices, regions and styles. Reduce bias: add varied examples, check who is missing, test on many groups, fix wrong labels and listen to complaints.

NameDateTime takenScore ___ / 40
  1. 1.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) few child voices in data (d) it was a rainy day
  2. 2.🔍 Which is the odd one out? (a) test on many groups (b) use one group only (c) add more of the same (d) copy old unfair choices
  3. 3.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  4. 4.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) the weather (c) its training data (d) its colour
  5. 5.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  6. 6.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  7. 7.🔍 Which is the odd one out? (a) add more of the same (b) skip testing (c) add varied examples (d) ignore complaints
  8. 8.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  9. 9.🔍 Which is the odd one out? (a) copy old unfair choices (b) check who is missing (c) add varied examples (d) ask different people
  10. 10.🔍 Which is the odd one out? (a) add more of the same (b) skip testing (c) ask different people (d) copy old unfair choices
  11. 11.🔍 Which is the odd one out? (a) balance the data (b) fix wrong labels (c) use one group only (d) test on many groups
  12. 12.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  13. 13.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  14. 14.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  15. 15.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  16. 16.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) bias from data (c) a strong battery (d) a broken screen
  17. 17.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  18. 18.Which of these helps reduce bias? (a) copy old unfair choices (b) add more of the same (c) fix wrong labels (d) use one group only
  19. 19.Which of these helps reduce bias? (a) skip testing (b) add varied examples (c) test on one group only (d) ignore complaints
  20. 20.Which of these helps reduce bias? (a) add more of the same (b) ignore complaints (c) copy old unfair choices (d) ask different people
  21. 21.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  23. 23.🔍 Which is the odd one out? (a) balance the data (b) check who is missing (c) fix wrong labels (d) ignore complaints
  24. 24.True or false: “balance the data” adds to bias. (a) True (b) False
  25. 25.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  26. 26.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  27. 27.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  28. 28.True or false: “balance the data” helps reduce bias. (a) True (b) False
  29. 29.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  30. 30.True or false: “add varied examples” adds to bias. (a) True (b) False
  31. 31.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  32. 32.Which of these adds to bias? (a) skip testing (b) add varied examples (c) test on many groups (d) balance the data
  33. 33.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  34. 34.🔍 Which is the odd one out? (a) test on one group only (b) balance the data (c) copy old unfair choices (d) add more of the same
  35. 35.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  36. 36.🔍 Which is the odd one out? (a) copy old unfair choices (b) fix wrong labels (c) skip testing (d) use one group only
  37. 37.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  38. 38.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  39. 39.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  40. 40.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False

Answer key

  1. few child voices in data
  2. test on many groups
  3. False
  4. its training data
  5. reduces bias
  6. False
  7. add varied examples
  8. reduces bias
  9. copy old unfair choices
  10. ask different people
  11. use one group only
  12. False
  13. True
  14. True
  15. True
  16. bias from data
  17. True
  18. fix wrong labels
  19. add varied examples
  20. ask different people
  21. True
  22. adds to bias
  23. ignore complaints
  24. False
  25. adds to bias
  26. one-sided data
  27. True
  28. True
  29. balanced data
  30. False
  31. adds to bias
  32. skip testing
  33. adds to bias
  34. balance the data
  35. True
  36. fix wrong labels
  37. reduces bias
  38. balanced data
  39. balanced data
  40. False

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