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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 191 · 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) test on many groups (b) skip testing (c) use one group only (d) add more of the same
  2. 2.Which of these adds to bias? (a) add varied examples (b) ignore complaints (c) ask different people (d) test on many groups
  3. 3.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  4. 4.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) add more of the same (d) check who is missing
  5. 5.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  6. 6.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  7. 7.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  8. 8.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  9. 9.🔍 Which is the odd one out? (a) test on one group only (b) add varied examples (c) add more of the same (d) copy old unfair choices
  10. 10.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its screen size (d) its training data
  11. 11.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) ask different people (d) use one group only
  12. 12.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) ask different people (d) skip testing
  13. 13.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  14. 14.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  15. 15.True or false: “ask different people” helps reduce bias. (a) True (b) False
  16. 16.Which of these helps reduce bias? (a) ignore complaints (b) test on one group only (c) use one group only (d) test on many groups
  17. 17.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  18. 18.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  19. 19.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  20. 20.True or false: “skip testing” adds to bias. (a) True (b) False
  21. 21.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  22. 22.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  23. 23.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  24. 24.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  25. 25.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  26. 26.🗣️ 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
  27. 27.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  28. 28.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  29. 29.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  30. 30.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  31. 31.Which of these helps reduce bias? (a) skip testing (b) use one group only (c) add varied examples (d) copy old unfair choices
  32. 32.True or false: “ask different people” adds to bias. (a) False (b) True
  33. 33.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
  34. 34.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  35. 35.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  36. 36.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  37. 37.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  38. 38.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  39. 39.True or false: “test on many groups” adds to bias. (a) False (b) True
  40. 40.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False

Answer key

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

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