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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 99 · 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.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  2. 2.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  3. 3.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) its colour (d) the weather
  4. 4.Which of these helps reduce bias? (a) add more of the same (b) use one group only (c) balance the data (d) test on one group only
  5. 5.🔍 Which is the odd one out? (a) check who is missing (b) add more of the same (c) fix wrong labels (d) balance the data
  6. 6.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  7. 7.True or false: “use one group only” adds to bias. (a) True (b) False
  8. 8.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
  9. 9.🔍 Which is the odd one out? (a) copy old unfair choices (b) balance the data (c) fix wrong labels (d) check who is missing
  10. 10.🔍 Which is the odd one out? (a) ignore complaints (b) balance the data (c) add varied examples (d) check who is missing
  11. 11.🔍 Which is the odd one out? (a) skip testing (b) ignore complaints (c) fix wrong labels (d) test on one group only
  12. 12.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
  13. 13.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  14. 14.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  15. 15.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  16. 16.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  17. 17.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  18. 18.True or false: “ask different people” adds to bias. (a) False (b) True
  19. 19.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  20. 20.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  21. 21.🔍 Which is the odd one out? (a) use one group only (b) add more of the same (c) ignore complaints (d) ask different people
  22. 22.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  23. 23.Which of these adds to bias? (a) balance the data (b) test on many groups (c) copy old unfair choices (d) add varied examples
  24. 24.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  25. 25.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  26. 26.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) fewer tests (d) one group only
  27. 27.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) it was a rainy day (d) few child voices in data
  28. 28.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  29. 29.🔍 Which is the odd one out? (a) use one group only (b) check who is missing (c) copy old unfair choices (d) skip testing
  30. 30.Which of these adds to bias? (a) ask different people (b) skip testing (c) fix wrong labels (d) check who is missing
  31. 31.Which of these adds to bias? (a) check who is missing (b) ask different people (c) test on one group only (d) test on many groups
  32. 32.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  33. 33.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  34. 34.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a broken screen (d) a fair model
  35. 35.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  36. 36.True or false: “balance the data” adds to bias. (a) True (b) False
  37. 37.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  38. 38.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  39. 39.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) add varied examples (d) skip testing
  40. 40.True or false: “add more of the same” adds to bias. (a) False (b) True

Answer key

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

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