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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 261 · 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.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) ask different people (d) skip testing
  2. 2.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  3. 3.True or false: “balance the data” helps reduce bias. (a) False (b) True
  4. 4.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  5. 5.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
  6. 6.True or false: “test on many groups” adds to bias. (a) True (b) False
  7. 7.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) use one group only (d) test on one group only
  8. 8.🔍 Which is the odd one out? (a) copy old unfair choices (b) skip testing (c) ignore complaints (d) check who is missing
  9. 9.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  10. 10.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  11. 11.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  12. 12.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  13. 13.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  14. 14.True or false: “ask different people” helps reduce bias. (a) True (b) False
  15. 15.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  16. 16.Which of these helps reduce bias? (a) use one group only (b) ignore complaints (c) copy old unfair choices (d) check who is missing
  17. 17.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) ask different people (d) copy old unfair choices
  18. 18.True or false: “skip testing” helps reduce bias. (a) True (b) False
  19. 19.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  20. 20.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) test on many groups (d) add more of the same
  21. 21.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) few child voices in data (d) it was a rainy day
  22. 22.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  23. 23.Which of these adds to bias? (a) ignore complaints (b) test on many groups (c) check who is missing (d) fix wrong labels
  24. 24.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  25. 25.🔍 Which is the odd one out? (a) copy old unfair choices (b) add varied examples (c) skip testing (d) test on one group only
  26. 26.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) ask different people (d) use one group only
  27. 27.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  28. 28.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  29. 29.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a broken screen (c) a fair model (d) a strong battery
  30. 30.True or false: “ask different people” adds to bias. (a) True (b) False
  31. 31.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  32. 32.Which of these adds to bias? (a) test on one group only (b) test on many groups (c) ask different people (d) fix wrong labels
  33. 33.Which of these adds to bias? (a) ask different people (b) test on many groups (c) skip testing (d) check who is missing
  34. 34.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  35. 35.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  36. 36.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  37. 37.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
  38. 38.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  39. 39.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  40. 40.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data

Answer key

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

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