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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 825 · 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: photos of people from only one age group is one-sided data (likely to make a biased model). (a) False (b) True
  2. 2.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  3. 3.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) its screen size (d) the weather
  4. 4.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  5. 5.True or false: “balance the data” adds to bias. (a) True (b) False
  6. 6.🔍 Which is the odd one out? (a) add more of the same (b) check who is missing (c) use one group only (d) skip testing
  7. 7.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  8. 8.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  9. 9.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
  10. 10.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  11. 11.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  12. 12.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  13. 13.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
  14. 14.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) more varied data (d) ignoring mistakes
  15. 15.True or false: “add varied examples” adds to bias. (a) False (b) True
  16. 16.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  17. 17.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) fix wrong labels (d) ignore complaints
  18. 18.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  19. 19.True or false: “use one group only” helps reduce bias. (a) True (b) False
  20. 20.🔍 Which is the odd one out? (a) add more of the same (b) test on many groups (c) check who is missing (d) add varied examples
  21. 21.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  22. 22.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  23. 23.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  24. 24.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  25. 25.True or false: “add more of the same” adds to bias. (a) False (b) True
  26. 26.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  27. 27.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  28. 28.Which of these helps reduce bias? (a) ask different people (b) skip testing (c) copy old unfair choices (d) test on one group only
  29. 29.🔍 Which is the odd one out? (a) use one group only (b) balance the data (c) ignore complaints (d) add more of the same
  30. 30.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  31. 31.Which of these helps reduce bias? (a) use one group only (b) ignore complaints (c) check who is missing (d) skip testing
  32. 32.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  33. 33.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  34. 34.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) fix wrong labels (d) test on one group only
  35. 35.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  36. 36.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) ask different people (d) copy old unfair choices
  37. 37.True or false: “ask different people” helps reduce bias. (a) False (b) True
  38. 38.Which of these adds to bias? (a) balance the data (b) fix wrong labels (c) add more of the same (d) test on many groups
  39. 39.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) few child voices in data (c) it was a rainy day (d) the phone was new
  40. 40.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias

Answer key

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

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