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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 819 · 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: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  2. 2.🔍 Which is the odd one out? (a) use one group only (b) fix wrong labels (c) balance the data (d) test on many groups
  3. 3.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  4. 4.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
  5. 5.🔍 Which is the odd one out? (a) ask different people (b) test on many groups (c) check who is missing (d) ignore complaints
  6. 6.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) bias from data (c) a strong battery (d) a broken screen
  7. 7.🔍 Which is the odd one out? (a) check who is missing (b) balance the data (c) test on one group only (d) add varied examples
  8. 8.True or false: “ask different people” adds to bias. (a) True (b) False
  9. 9.🔍 Which is the odd one out? (a) fix wrong labels (b) test on many groups (c) check who is missing (d) skip testing
  10. 10.True or false: “add more of the same” adds to bias. (a) True (b) False
  11. 11.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  12. 12.Which of these adds to bias? (a) test on one group only (b) add varied examples (c) ask different people (d) fix wrong labels
  13. 13.🔍 Which is the odd one out? (a) skip testing (b) copy old unfair choices (c) ignore complaints (d) test on many groups
  14. 14.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  15. 15.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  16. 16.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  17. 17.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  18. 18.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  19. 19.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  20. 20.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  21. 21.🔍 Which is the odd one out? (a) check who is missing (b) add more of the same (c) ignore complaints (d) test on one group only
  22. 22.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  23. 23.🔍 Which is the odd one out? (a) add more of the same (b) fix wrong labels (c) test on many groups (d) ask different people
  24. 24.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  25. 25.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  26. 26.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  27. 27.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  28. 28.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  29. 29.Which of these helps reduce bias? (a) skip testing (b) add varied examples (c) test on one group only (d) ignore complaints
  30. 30.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  31. 31.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  32. 32.True or false: “ignore complaints” adds to bias. (a) True (b) False
  33. 33.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  34. 34.Which of these helps reduce bias? (a) copy old unfair choices (b) balance the data (c) skip testing (d) ignore complaints
  35. 35.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) check who is missing (d) ignore complaints
  36. 36.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) ask different people (d) copy old unfair choices
  37. 37.Which of these helps reduce bias? (a) add more of the same (b) fix wrong labels (c) test on one group only (d) ignore complaints
  38. 38.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  39. 39.True or false: “skip testing” helps reduce bias. (a) True (b) False
  40. 40.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) ignoring mistakes (d) more varied data

Answer key

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

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