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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 863 · 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: copy old unfair choices? (a) reduces bias (b) adds to bias
  2. 2.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) bias from data (c) a strong battery (d) a fair model
  3. 3.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  4. 4.True or false: “use one group only” adds to bias. (a) False (b) True
  5. 5.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) copy old unfair choices (d) ask different people
  6. 6.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its colour (c) the weather (d) its screen size
  7. 7.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  8. 8.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  9. 9.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  10. 10.🗣️ 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
  11. 11.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  12. 12.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
  13. 13.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  14. 14.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  15. 15.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
  16. 16.Which of these adds to bias? (a) use one group only (b) fix wrong labels (c) check who is missing (d) add varied examples
  17. 17.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  18. 18.True or false: “use one group only” helps reduce bias. (a) True (b) False
  19. 19.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on one group only (c) ignore complaints (d) balance the data
  20. 20.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  21. 21.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  22. 22.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  23. 23.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) ignoring mistakes (d) one group only
  24. 24.Which of these adds to bias? (a) check who is missing (b) balance the data (c) skip testing (d) ask different people
  25. 25.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  26. 26.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  27. 27.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  28. 28.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  29. 29.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  30. 30.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  31. 31.Which of these adds to bias? (a) check who is missing (b) ask different people (c) test on many groups (d) add more of the same
  32. 32.True or false: “balance the data” helps reduce bias. (a) False (b) True
  33. 33.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  34. 34.🔍 Which is the odd one out? (a) use one group only (b) add varied examples (c) fix wrong labels (d) check who is missing
  35. 35.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  36. 36.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  37. 37.Which of these helps reduce bias? (a) copy old unfair choices (b) skip testing (c) add more of the same (d) balance the data
  38. 38.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  39. 39.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  40. 40.True or false: “skip testing” helps reduce bias. (a) False (b) True

Answer key

  1. adds to bias
  2. bias from data
  3. False
  4. True
  5. copy old unfair choices
  6. its training data
  7. False
  8. False
  9. adds to bias
  10. few child voices in data
  11. False
  12. True
  13. reduces bias
  14. False
  15. True
  16. use one group only
  17. True
  18. False
  19. balance the data
  20. reduces bias
  21. True
  22. False
  23. more varied data
  24. skip testing
  25. adds to bias
  26. one-sided data
  27. False
  28. adds to bias
  29. True
  30. True
  31. add more of the same
  32. True
  33. False
  34. use one group only
  35. False
  36. False
  37. balance the data
  38. True
  39. balanced data
  40. False

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