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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 750 · 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: “skip testing” adds to bias. (a) False (b) True
  2. 2.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  3. 3.Which of these adds to bias? (a) ask different people (b) balance the data (c) fix wrong labels (d) ignore complaints
  4. 4.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  5. 5.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) use one group only (d) add varied examples
  6. 6.Which of these adds to bias? (a) add more of the same (b) fix wrong labels (c) add varied examples (d) test on many groups
  7. 7.🔍 Which is the odd one out? (a) add more of the same (b) use one group only (c) skip testing (d) add varied examples
  8. 8.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its screen size (d) its colour
  9. 9.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) test on many groups (d) add more of the same
  10. 10.🔍 Which is the odd one out? (a) add varied examples (b) copy old unfair choices (c) fix wrong labels (d) balance the data
  11. 11.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  12. 12.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  13. 13.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  14. 14.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  15. 15.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  16. 16.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  17. 17.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) test on many groups (d) use one group only
  18. 18.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  19. 19.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  20. 20.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  21. 21.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) copy old unfair choices (d) check who is missing
  22. 22.Which of these helps reduce bias? (a) ignore complaints (b) balance the data (c) add more of the same (d) skip testing
  23. 23.🔍 Which is the odd one out? (a) ignore complaints (b) test on many groups (c) check who is missing (d) add varied examples
  24. 24.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  25. 25.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  26. 26.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  27. 27.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) more varied data (d) ignoring mistakes
  28. 28.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  29. 29.Which of these adds to bias? (a) fix wrong labels (b) copy old unfair choices (c) add varied examples (d) check who is missing
  30. 30.🔍 Which is the odd one out? (a) balance the data (b) ask different people (c) skip testing (d) fix wrong labels
  31. 31.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
  32. 32.True or false: “use one group only” helps reduce bias. (a) False (b) True
  33. 33.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  34. 34.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) test on one group only (d) add varied examples
  35. 35.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  36. 36.Which of these adds to bias? (a) test on many groups (b) use one group only (c) balance the data (d) check who is missing
  37. 37.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  38. 38.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  39. 39.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
  40. 40.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data

Answer key

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

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