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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 67 · 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 is the odd one out? (a) use one group only (b) test on many groups (c) add more of the same (d) ignore complaints
  2. 2.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  3. 3.True or false: “use one group only” adds to bias. (a) True (b) False
  4. 4.Which of these adds to bias? (a) skip testing (b) test on many groups (c) fix wrong labels (d) ask different people
  5. 5.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  6. 6.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  7. 7.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  8. 8.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
  9. 9.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  10. 10.True or false: “ask different people” adds to bias. (a) True (b) False
  11. 11.True or false: “add varied examples” adds to bias. (a) False (b) True
  12. 12.True or false: “add more of the same” adds to bias. (a) True (b) False
  13. 13.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  14. 14.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its screen size (d) its training data
  15. 15.🔍 Which is the odd one out? (a) fix wrong labels (b) add varied examples (c) ignore complaints (d) balance the data
  16. 16.Which of these helps reduce bias? (a) skip testing (b) fix wrong labels (c) use one group only (d) add more of the same
  17. 17.🔍 Which is the odd one out? (a) fix wrong labels (b) add more of the same (c) balance the data (d) add varied examples
  18. 18.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  19. 19.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  20. 20.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  21. 21.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  22. 22.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  23. 23.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  24. 24.🔍 Which is the odd one out? (a) test on one group only (b) fix wrong labels (c) skip testing (d) copy old unfair choices
  25. 25.🔍 Which is the odd one out? (a) copy old unfair choices (b) ask different people (c) balance the data (d) add varied examples
  26. 26.Which of these helps reduce bias? (a) balance the data (b) skip testing (c) ignore complaints (d) use one group only
  27. 27.Which of these adds to bias? (a) use one group only (b) fix wrong labels (c) test on many groups (d) ask different people
  28. 28.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) False (b) True
  29. 29.Which of these adds to bias? (a) balance the data (b) add varied examples (c) test on one group only (d) ask different people
  30. 30.🔍 Which is the odd one out? (a) ask different people (b) use one group only (c) skip testing (d) copy old unfair choices
  31. 31.Which of these adds to bias? (a) add more of the same (b) add varied examples (c) check who is missing (d) test on many groups
  32. 32.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  33. 33.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  34. 34.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  35. 35.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) ignoring mistakes (d) one group only
  36. 36.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  37. 37.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
  38. 38.🔍 Which is the odd one out? (a) test on one group only (b) fix wrong labels (c) test on many groups (d) add varied examples
  39. 39.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) use one group only (d) ask different people
  40. 40.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias

Answer key

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

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