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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 971 · 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.🔍 Which is the odd one out? (a) add varied examples (b) fix wrong labels (c) test on one group only (d) check who is missing
  2. 2.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  3. 3.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) fewer tests (d) more varied data
  4. 4.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  5. 5.🔍 Which is the odd one out? (a) add varied examples (b) use one group only (c) skip testing (d) copy old unfair choices
  6. 6.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  7. 7.Which of these helps reduce bias? (a) add varied examples (b) copy old unfair choices (c) use one group only (d) test on one group only
  8. 8.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  9. 9.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  10. 10.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) add more of the same (d) fix wrong labels
  11. 11.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  12. 12.Which of these adds to bias? (a) balance the data (b) ignore complaints (c) check who is missing (d) add varied examples
  13. 13.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  14. 14.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  15. 15.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  16. 16.🔍 Which is the odd one out? (a) add more of the same (b) fix wrong labels (c) add varied examples (d) balance the data
  17. 17.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  18. 18.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  19. 19.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  20. 20.Which of these helps reduce bias? (a) test on many groups (b) skip testing (c) test on one group only (d) use one group only
  21. 21.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  22. 22.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its colour (d) its screen size
  23. 23.True or false: “use one group only” helps reduce bias. (a) True (b) False
  24. 24.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  25. 25.Which of these adds to bias? (a) balance the data (b) add more of the same (c) test on many groups (d) fix wrong labels
  26. 26.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  27. 27.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
  28. 28.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  29. 29.True or false: “use one group only” adds to bias. (a) False (b) True
  30. 30.True or false: “ask different people” adds to bias. (a) True (b) False
  31. 31.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
  32. 32.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  33. 33.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  34. 34.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) add varied examples (d) skip testing
  35. 35.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  36. 36.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  37. 37.🔍 Which is the odd one out? (a) skip testing (b) fix wrong labels (c) use one group only (d) ignore complaints
  38. 38.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
  39. 39.True or false: “skip testing” adds to bias. (a) True (b) False
  40. 40.True or false: “check who is missing” adds to bias. (a) True (b) False

Answer key

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

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