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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 871 · 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: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  2. 2.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  3. 3.True or false: “check who is missing” adds to bias. (a) True (b) False
  4. 4.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) the weather (d) its training data
  5. 5.🔍 Which is the odd one out? (a) test on one group only (b) copy old unfair choices (c) use one group only (d) ask different people
  6. 6.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  7. 7.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) fix wrong labels (d) skip testing
  8. 8.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  9. 9.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  10. 10.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  11. 11.🔍 Which is the odd one out? (a) add varied examples (b) use one group only (c) skip testing (d) copy old unfair choices
  12. 12.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) add more of the same (d) check who is missing
  13. 13.True or false: “ask different people” adds to bias. (a) True (b) False
  14. 14.Which of these helps reduce bias? (a) test on one group only (b) balance the data (c) skip testing (d) use one group only
  15. 15.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  16. 16.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on one group only (c) ignore complaints (d) check who is missing
  17. 17.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  18. 18.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  19. 19.🔍 Which is the odd one out? (a) use one group only (b) check who is missing (c) add varied examples (d) test on many groups
  20. 20.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
  21. 21.Which of these adds to bias? (a) balance the data (b) add varied examples (c) copy old unfair choices (d) test on many groups
  22. 22.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) the phone was new (c) few child voices in data (d) children speak too fast
  23. 23.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  24. 24.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
  25. 25.🔍 Which is the odd one out? (a) balance the data (b) add more of the same (c) test on many groups (d) add varied examples
  26. 26.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  27. 27.True or false: “skip testing” helps reduce bias. (a) True (b) False
  28. 28.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  29. 29.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
  30. 30.Which of these helps reduce bias? (a) test on many groups (b) add more of the same (c) test on one group only (d) use one group only
  31. 31.True or false: “skip testing” adds to bias. (a) False (b) True
  32. 32.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) test on many groups (d) test on one group only
  33. 33.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  34. 34.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  35. 35.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) ignoring mistakes (d) more varied data
  36. 36.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  37. 37.🔍 Which is the odd one out? (a) balance the data (b) skip testing (c) ignore complaints (d) use one group only
  38. 38.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  39. 39.🔍 Which is the odd one out? (a) copy old unfair choices (b) add varied examples (c) test on many groups (d) fix wrong labels
  40. 40.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data

Answer key

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

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