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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 271 · 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: “test on one group only” helps reduce bias. (a) True (b) False
  2. 2.Which of these adds to bias? (a) add varied examples (b) copy old unfair choices (c) test on many groups (d) fix wrong labels
  3. 3.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  4. 4.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  5. 5.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  6. 6.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  7. 7.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  8. 8.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
  9. 9.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) check who is missing (d) skip testing
  10. 10.True or false: “skip testing” helps reduce bias. (a) False (b) True
  11. 11.🔍 Which is the odd one out? (a) use one group only (b) balance the data (c) fix wrong labels (d) add varied examples
  12. 12.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
  13. 13.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its colour (d) its screen size
  14. 14.🔍 Which is the odd one out? (a) add varied examples (b) fix wrong labels (c) check who is missing (d) test on one group only
  15. 15.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) ask different people (d) add more of the same
  16. 16.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  17. 17.🔍 Which is the odd one out? (a) ignore complaints (b) use one group only (c) add more of the same (d) check who is missing
  18. 18.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  19. 19.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  20. 20.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  21. 21.True or false: “use one group only” adds to bias. (a) False (b) True
  22. 22.Which of these helps reduce bias? (a) ignore complaints (b) balance the data (c) use one group only (d) add more of the same
  23. 23.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  24. 24.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
  25. 25.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  26. 26.🔍 Which is the odd one out? (a) skip testing (b) ignore complaints (c) use one group only (d) fix wrong labels
  27. 27.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) test on one group only (d) balance the data
  28. 28.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  29. 29.True or false: “add varied examples” adds to bias. (a) True (b) False
  30. 30.🔍 Which is the odd one out? (a) use one group only (b) test on many groups (c) add more of the same (d) skip testing
  31. 31.True or false: “add more of the same” adds to bias. (a) False (b) True
  32. 32.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  33. 33.Which of these helps reduce bias? (a) test on one group only (b) check who is missing (c) skip testing (d) add more of the same
  34. 34.🔍 Which is the odd one out? (a) add varied examples (b) fix wrong labels (c) ignore complaints (d) balance the data
  35. 35.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  36. 36.Which of these helps reduce bias? (a) add more of the same (b) copy old unfair choices (c) skip testing (d) fix wrong labels
  37. 37.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  38. 38.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  39. 39.True or false: “add more of the same” helps reduce bias. (a) False (b) True
  40. 40.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data

Answer key

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

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