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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 965 · 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: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  2. 2.True or false: “add varied examples” adds to bias. (a) True (b) False
  3. 3.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
  4. 4.True or false: “skip testing” adds to bias. (a) True (b) False
  5. 5.True or false: “test on one group only” adds to bias. (a) False (b) True
  6. 6.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) use one group only (d) add varied examples
  7. 7.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  8. 8.🔍 Which is the odd one out? (a) test on one group only (b) ask different people (c) fix wrong labels (d) check who is missing
  9. 9.Which of these adds to bias? (a) balance the data (b) add varied examples (c) check who is missing (d) use one group only
  10. 10.Which of these adds to bias? (a) fix wrong labels (b) test on many groups (c) copy old unfair choices (d) ask different people
  11. 11.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  12. 12.Which of these helps reduce bias? (a) add varied examples (b) use one group only (c) ignore complaints (d) test on one group only
  13. 13.🔍 Which is the odd one out? (a) test on many groups (b) test on one group only (c) ignore complaints (d) skip testing
  14. 14.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) fewer tests (d) more varied data
  15. 15.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  16. 16.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  17. 17.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  18. 18.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) the weather (c) its screen size (d) its training data
  19. 19.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  20. 20.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  21. 21.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
  22. 22.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  23. 23.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  24. 24.🔍 Which is the odd one out? (a) skip testing (b) add varied examples (c) ignore complaints (d) test on one group only
  25. 25.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  26. 26.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  27. 27.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  28. 28.Which of these adds to bias? (a) balance the data (b) test on many groups (c) fix wrong labels (d) skip testing
  29. 29.True or false: “skip testing” helps reduce bias. (a) False (b) True
  30. 30.Which of these helps reduce bias? (a) ignore complaints (b) copy old unfair choices (c) fix wrong labels (d) use one group only
  31. 31.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  32. 32.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) children speak too fast (d) few child voices in data
  33. 33.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  34. 34.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  35. 35.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  36. 36.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  37. 37.True or false: “ask different people” helps reduce bias. (a) True (b) False
  38. 38.🔍 Which is the odd one out? (a) ignore complaints (b) add more of the same (c) ask different people (d) skip testing
  39. 39.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
  40. 40.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias

Answer key

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

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