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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 316 · 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.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) it was a rainy day (d) few child voices in data
  2. 2.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its screen size (d) its colour
  3. 3.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  4. 4.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) ignoring mistakes (d) more varied data
  5. 5.Which of these adds to bias? (a) add varied examples (b) ask different people (c) skip testing (d) test on many groups
  6. 6.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  7. 7.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  8. 8.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  9. 9.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  10. 10.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) copy old unfair choices (d) add varied examples
  11. 11.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  12. 12.True or false: “balance the data” adds to bias. (a) False (b) True
  13. 13.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  14. 14.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  15. 15.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) ask different people (d) skip testing
  16. 16.True or false: “test on one group only” adds to bias. (a) False (b) True
  17. 17.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  18. 18.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  19. 19.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  20. 20.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  21. 21.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  22. 22.🔍 Which is the odd one out? (a) skip testing (b) balance the data (c) check who is missing (d) ask different people
  23. 23.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  24. 24.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  25. 25.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  26. 26.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  27. 27.True or false: “ask different people” helps reduce bias. (a) True (b) False
  28. 28.True or false: “add varied examples” adds to bias. (a) True (b) False
  29. 29.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  30. 30.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  31. 31.Which of these helps reduce bias? (a) check who is missing (b) copy old unfair choices (c) ignore complaints (d) skip testing
  32. 32.Which of these helps reduce bias? (a) ignore complaints (b) copy old unfair choices (c) test on many groups (d) use one group only
  33. 33.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  34. 34.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  35. 35.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  36. 36.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  37. 37.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
  38. 38.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  39. 39.🔍 Which is the odd one out? (a) copy old unfair choices (b) add more of the same (c) use one group only (d) test on many groups
  40. 40.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias

Answer key

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

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