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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 600 · 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: “ask different people” adds to bias. (a) True (b) False
  2. 2.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  3. 3.True or false: “ignore complaints” adds to bias. (a) False (b) True
  4. 4.🔍 Which is the odd one out? (a) ask different people (b) add more of the same (c) fix wrong labels (d) test on many groups
  5. 5.Which of these helps reduce bias? (a) skip testing (b) use one group only (c) test on one group only (d) add varied examples
  6. 6.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  7. 7.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
  8. 8.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) few child voices in data (d) the phone was new
  9. 9.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  10. 10.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  11. 11.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  12. 12.Which of these adds to bias? (a) fix wrong labels (b) skip testing (c) test on many groups (d) check who is missing
  13. 13.🛠️ What helps make an AI system fairer? (a) more varied data (b) ignoring mistakes (c) one group only (d) fewer tests
  14. 14.🔍 Which is the odd one out? (a) add varied examples (b) ignore complaints (c) fix wrong labels (d) test on many groups
  15. 15.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  16. 16.🔍 Which is the odd one out? (a) test on one group only (b) ignore complaints (c) copy old unfair choices (d) ask different people
  17. 17.True or false: “test on one group only” adds to bias. (a) True (b) False
  18. 18.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
  19. 19.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  20. 20.🔍 Which is the odd one out? (a) add varied examples (b) check who is missing (c) use one group only (d) ask different people
  21. 21.True or false: “add more of the same” 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.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) its screen size (d) the weather
  24. 24.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) skip testing (d) add more of the same
  25. 25.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
  26. 26.Which of these adds to bias? (a) add varied examples (b) ask different people (c) ignore complaints (d) check who is missing
  27. 27.True or false: “balance the data” adds to bias. (a) True (b) False
  28. 28.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  29. 29.Which of these helps reduce bias? (a) test on one group only (b) add more of the same (c) skip testing (d) check who is missing
  30. 30.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  31. 31.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  32. 32.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  33. 33.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  34. 34.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  35. 35.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) ask different people (d) copy old unfair choices
  36. 36.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  37. 37.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  38. 38.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  39. 39.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  40. 40.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias

Answer key

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

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