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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 184 · 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.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  2. 2.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  3. 3.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  4. 4.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) balance the data (d) use one group only
  5. 5.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  6. 6.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  7. 7.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  8. 8.🔍 Which is the odd one out? (a) ignore complaints (b) skip testing (c) test on many groups (d) test on one group only
  9. 9.True or false: “test on one group only” adds to bias. (a) True (b) False
  10. 10.True or false: “balance the data” adds to bias. (a) False (b) True
  11. 11.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  12. 12.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) its training data (d) the weather
  13. 13.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  14. 14.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) ask different people (d) copy old unfair choices
  15. 15.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
  16. 16.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  17. 17.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) one group only (d) fewer tests
  18. 18.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  19. 19.🔍 Which is the odd one out? (a) skip testing (b) balance the data (c) test on many groups (d) add varied examples
  20. 20.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  21. 21.True or false: “add varied examples” adds to bias. (a) False (b) True
  22. 22.🔍 Which is the odd one out? (a) ask different people (b) ignore complaints (c) test on many groups (d) add varied examples
  23. 23.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  24. 24.True or false: “balance the data” helps reduce bias. (a) False (b) True
  25. 25.Which of these helps reduce bias? (a) ignore complaints (b) check who is missing (c) use one group only (d) test on one group only
  26. 26.🔍 Which is the odd one out? (a) balance the data (b) skip testing (c) add more of the same (d) use one group only
  27. 27.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  28. 28.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  29. 29.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  30. 30.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  31. 31.🔍 Which is the odd one out? (a) test on one group only (b) ask different people (c) test on many groups (d) balance the data
  32. 32.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  33. 33.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  34. 34.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  35. 35.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  36. 36.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  37. 37.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  38. 38.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  39. 39.🔍 Which is the odd one out? (a) test on many groups (b) ask different people (c) check who is missing (d) copy old unfair choices
  40. 40.🔍 Which is the odd one out? (a) fix wrong labels (b) use one group only (c) ask different people (d) balance the data

Answer key

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

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