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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 790 · 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: “test on many groups” adds to bias. (a) False (b) True
  2. 2.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  3. 3.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  4. 4.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  5. 5.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  6. 6.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  7. 7.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) the weather (d) its training data
  8. 8.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) the phone was new (c) children speak too fast (d) it was a rainy day
  9. 9.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
  10. 10.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  11. 11.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  12. 12.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  13. 13.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  14. 14.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  15. 15.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  16. 16.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  17. 17.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) test on many groups (d) use one group only
  18. 18.True or false: “add more of the same” helps reduce bias. (a) False (b) True
  19. 19.Which of these adds to bias? (a) check who is missing (b) ask different people (c) copy old unfair choices (d) test on many groups
  20. 20.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) skip testing (d) ask different people
  21. 21.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  22. 22.🔍 Which is the odd one out? (a) skip testing (b) test on one group only (c) fix wrong labels (d) copy old unfair choices
  23. 23.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
  24. 24.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  25. 25.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  26. 26.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  27. 27.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
  28. 28.True or false: “ask different people” adds to bias. (a) True (b) False
  29. 29.🔍 Which is the odd one out? (a) skip testing (b) add more of the same (c) use one group only (d) balance the data
  30. 30.Which of these adds to bias? (a) check who is missing (b) fix wrong labels (c) balance the data (d) use one group only
  31. 31.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  32. 32.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  33. 33.Which of these helps reduce bias? (a) balance the data (b) ignore complaints (c) copy old unfair choices (d) add more of the same
  34. 34.🔍 Which is the odd one out? (a) test on many groups (b) check who is missing (c) skip testing (d) fix wrong labels
  35. 35.True or false: “add more of the same” adds to bias. (a) True (b) False
  36. 36.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  37. 37.True or false: “skip testing” adds to bias. (a) False (b) True
  38. 38.🔍 Which is the odd one out? (a) ignore complaints (b) test on many groups (c) check who is missing (d) fix wrong labels
  39. 39.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  40. 40.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True

Answer key

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

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