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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 262 · 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.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  2. 2.🔍 Which is the odd one out? (a) balance the data (b) add more of the same (c) add varied examples (d) test on many groups
  3. 3.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) the weather (d) its screen size
  4. 4.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  5. 5.True or false: “ignore complaints” adds to bias. (a) False (b) True
  6. 6.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) fewer tests (d) one group only
  7. 7.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  8. 8.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  9. 9.Which of these helps reduce bias? (a) test on one group only (b) test on many groups (c) use one group only (d) add more of the same
  10. 10.True or false: “skip testing” adds to bias. (a) False (b) True
  11. 11.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  12. 12.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  13. 13.True or false: “add more of the same” adds to bias. (a) False (b) True
  14. 14.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
  15. 15.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  16. 16.🔍 Which is the odd one out? (a) copy old unfair choices (b) fix wrong labels (c) add varied examples (d) test on many groups
  17. 17.🔍 Which is the odd one out? (a) copy old unfair choices (b) add more of the same (c) use one group only (d) balance the data
  18. 18.Which of these helps reduce bias? (a) test on one group only (b) ask different people (c) use one group only (d) skip testing
  19. 19.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  20. 20.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  21. 21.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  22. 22.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
  23. 23.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  24. 24.Which of these adds to bias? (a) copy old unfair choices (b) ask different people (c) check who is missing (d) fix wrong labels
  25. 25.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  26. 26.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  27. 27.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  28. 28.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  29. 29.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  30. 30.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True
  31. 31.🖼️ 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
  32. 32.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) it was a rainy day (c) few child voices in data (d) the phone was new
  33. 33.🔍 Which is the odd one out? (a) ask different people (b) ignore complaints (c) check who is missing (d) fix wrong labels
  34. 34.Which of these adds to bias? (a) ask different people (b) check who is missing (c) fix wrong labels (d) add more of the same
  35. 35.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  36. 36.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  37. 37.Which of these helps reduce bias? (a) skip testing (b) add varied examples (c) test on one group only (d) add more of the same
  38. 38.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) use one group only (d) check who is missing
  39. 39.🔍 Which is the odd one out? (a) add varied examples (b) skip testing (c) copy old unfair choices (d) use one group only
  40. 40.True or false: “use one group only” helps reduce bias. (a) True (b) False

Answer key

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

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