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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 961 · 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.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) copy old unfair choices (d) test on many groups
  2. 2.Which of these adds to bias? (a) use one group only (b) add varied examples (c) test on many groups (d) ask different people
  3. 3.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  4. 4.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  5. 5.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True
  6. 6.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  7. 7.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  8. 8.🔍 Which is the odd one out? (a) ignore complaints (b) use one group only (c) add more of the same (d) fix wrong labels
  9. 9.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  10. 10.True or false: “add varied examples” adds to bias. (a) True (b) False
  11. 11.Which of these adds to bias? (a) add varied examples (b) ignore complaints (c) check who is missing (d) test on many groups
  12. 12.🔍 Which is the odd one out? (a) test on many groups (b) skip testing (c) check who is missing (d) ask different people
  13. 13.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  14. 14.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  15. 15.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
  16. 16.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  17. 17.True or false: “test on many groups” adds to bias. (a) True (b) False
  18. 18.🔍 Which is the odd one out? (a) fix wrong labels (b) test on many groups (c) balance the data (d) use one group only
  19. 19.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  20. 20.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its screen size (c) its colour (d) its training data
  21. 21.True or false: “skip testing” helps reduce bias. (a) True (b) False
  22. 22.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) test on one group only (d) ask different people
  23. 23.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  24. 24.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  25. 25.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  26. 26.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  27. 27.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) bias from data (d) a strong battery
  28. 28.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  29. 29.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  30. 30.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  31. 31.🛠️ What helps make an AI system fairer? (a) more varied data (b) fewer tests (c) one group only (d) ignoring mistakes
  32. 32.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  33. 33.🔍 Which is the odd one out? (a) add varied examples (b) copy old unfair choices (c) fix wrong labels (d) check who is missing
  34. 34.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  35. 35.True or false: “check who is missing” adds to bias. (a) False (b) True
  36. 36.Which of these helps reduce bias? (a) ask different people (b) skip testing (c) use one group only (d) add more of the same
  37. 37.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  38. 38.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) check who is missing (d) ignore complaints
  39. 39.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  40. 40.True or false: “use one group only” helps reduce bias. (a) True (b) False

Answer key

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

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