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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 313 · 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: ask different people? (a) adds to bias (b) reduces bias
  2. 2.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  3. 3.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  4. 4.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  5. 5.🔍 Which is the odd one out? (a) add more of the same (b) copy old unfair choices (c) test on one group only (d) add varied examples
  6. 6.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  7. 7.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  8. 8.Which of these helps reduce bias? (a) test on many groups (b) ignore complaints (c) add more of the same (d) skip testing
  9. 9.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) check who is missing (d) copy old unfair choices
  10. 10.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  11. 11.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
  12. 12.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  13. 13.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  14. 14.🔍 Which is the odd one out? (a) ask different people (b) test on one group only (c) check who is missing (d) test on many groups
  15. 15.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  16. 16.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
  17. 17.True or false: “test on many groups” adds to bias. (a) False (b) True
  18. 18.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  19. 19.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  20. 20.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  21. 21.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  22. 22.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  23. 23.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  24. 24.🛠️ What helps make an AI system fairer? (a) more varied data (b) ignoring mistakes (c) one group only (d) fewer tests
  25. 25.True or false: “skip testing” adds to bias. (a) False (b) True
  26. 26.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  27. 27.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  28. 28.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) skip testing (d) balance the data
  29. 29.Which of these helps reduce bias? (a) add more of the same (b) check who is missing (c) use one group only (d) ignore complaints
  30. 30.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its colour (d) its screen size
  31. 31.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  32. 32.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  33. 33.🔍 Which is the odd one out? (a) add more of the same (b) skip testing (c) test on many groups (d) use one group only
  34. 34.Which of these adds to bias? (a) check who is missing (b) add varied examples (c) balance the data (d) test on one group only
  35. 35.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  36. 36.True or false: “add more of the same” adds to bias. (a) False (b) True
  37. 37.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  38. 38.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  39. 39.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  40. 40.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data

Answer key

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

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