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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 312 · 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.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  2. 2.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  3. 3.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
  4. 4.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a strong battery (c) a broken screen (d) bias from data
  5. 5.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  6. 6.Which of these helps reduce bias? (a) fix wrong labels (b) ignore complaints (c) add more of the same (d) use one group only
  7. 7.True or false: “use one group only” adds to bias. (a) True (b) False
  8. 8.True or false: “test on many groups” adds to bias. (a) True (b) False
  9. 9.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) ignore complaints (d) add varied examples
  10. 10.Which of these helps reduce bias? (a) skip testing (b) check who is missing (c) test on one group only (d) use one group only
  11. 11.🔍 Which is the odd one out? (a) ignore complaints (b) add more of the same (c) add varied examples (d) test on one group only
  12. 12.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  13. 13.🔍 Which is the odd one out? (a) ask different people (b) balance the data (c) test on many groups (d) copy old unfair choices
  14. 14.🛠️ What helps make an AI system fairer? (a) more varied data (b) ignoring mistakes (c) one group only (d) fewer tests
  15. 15.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  16. 16.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  17. 17.Which of these adds to bias? (a) ask different people (b) add more of the same (c) check who is missing (d) balance the data
  18. 18.True or false: “skip testing” helps reduce bias. (a) True (b) False
  19. 19.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  20. 20.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) ask different people (d) skip testing
  21. 21.🔍 Which is the odd one out? (a) use one group only (b) fix wrong labels (c) copy old unfair choices (d) test on one group only
  22. 22.True or false: “balance the data” helps reduce bias. (a) False (b) True
  23. 23.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  24. 24.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  25. 25.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  26. 26.🔍 Which is the odd one out? (a) fix wrong labels (b) add varied examples (c) balance the data (d) use one group only
  27. 27.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  28. 28.Which of these helps reduce bias? (a) use one group only (b) add varied examples (c) ignore complaints (d) add more of the same
  29. 29.🔍 Which is the odd one out? (a) ignore complaints (b) check who is missing (c) add more of the same (d) use one group only
  30. 30.Which of these adds to bias? (a) add varied examples (b) ask different people (c) fix wrong labels (d) test on one group only
  31. 31.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  32. 32.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  33. 33.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  34. 34.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  35. 35.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) ask different people (d) copy old unfair choices
  36. 36.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  37. 37.True or false: “add varied examples” adds to bias. (a) True (b) False
  38. 38.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  39. 39.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
  40. 40.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True

Answer key

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

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