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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 800 · 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.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  2. 2.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) the weather (d) its screen size
  3. 3.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) ignore complaints (d) test on many groups
  4. 4.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
  5. 5.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  6. 6.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  7. 7.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  8. 8.Which of these adds to bias? (a) ignore complaints (b) add varied examples (c) fix wrong labels (d) balance the data
  9. 9.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  10. 10.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  11. 11.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  12. 12.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  13. 13.🔍 Which is the odd one out? (a) test on many groups (b) ask different people (c) balance the data (d) skip testing
  14. 14.True or false: “balance the data” adds to bias. (a) False (b) True
  15. 15.🔍 Which is the odd one out? (a) test on many groups (b) use one group only (c) add varied examples (d) balance the data
  16. 16.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  17. 17.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
  18. 18.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  19. 19.🔍 Which is the odd one out? (a) add varied examples (b) skip testing (c) add more of the same (d) ignore complaints
  20. 20.True or false: “ignore complaints” adds to bias. (a) False (b) True
  21. 21.🔍 Which is the odd one out? (a) add more of the same (b) fix wrong labels (c) skip testing (d) ignore complaints
  22. 22.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  23. 23.🔍 Which is the odd one out? (a) copy old unfair choices (b) ignore complaints (c) use one group only (d) test on many groups
  24. 24.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  25. 25.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) ask different people (d) test on one group only
  26. 26.True or false: “add more of the same” adds to bias. (a) True (b) False
  27. 27.Which of these adds to bias? (a) balance the data (b) test on many groups (c) copy old unfair choices (d) add varied examples
  28. 28.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  29. 29.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  30. 30.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) balance the data (d) test on one group only
  31. 31.🗣️ 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) the phone was new (d) few child voices in data
  32. 32.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  33. 33.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  34. 34.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  35. 35.True or false: “skip testing” helps reduce bias. (a) True (b) False
  36. 36.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
  37. 37.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  38. 38.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  39. 39.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  40. 40.Which of these helps reduce bias? (a) test on one group only (b) add more of the same (c) skip testing (d) ask different people

Answer key

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

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