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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 728 · 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 of these helps reduce bias? (a) add varied examples (b) ignore complaints (c) skip testing (d) copy old unfair choices
  2. 2.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  3. 3.True or false: “add more of the same” helps reduce bias. (a) False (b) True
  4. 4.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  5. 5.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  6. 6.🔍 Which is the odd one out? (a) ignore complaints (b) add varied examples (c) ask different people (d) balance the data
  7. 7.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  8. 8.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  9. 9.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  10. 10.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  11. 11.Which of these helps reduce bias? (a) add more of the same (b) copy old unfair choices (c) fix wrong labels (d) use one group only
  12. 12.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  13. 13.True or false: “balance the data” helps reduce bias. (a) True (b) False
  14. 14.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) add more of the same (d) add varied examples
  15. 15.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  16. 16.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  17. 17.🔍 Which is the odd one out? (a) test on one group only (b) balance the data (c) check who is missing (d) fix wrong labels
  18. 18.Which of these adds to bias? (a) balance the data (b) add varied examples (c) test on many groups (d) ignore complaints
  19. 19.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  20. 20.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) the weather (c) its training data (d) its screen size
  21. 21.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  22. 22.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  23. 23.True or false: “use one group only” helps reduce bias. (a) False (b) True
  24. 24.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  25. 25.Which of these adds to bias? (a) ask different people (b) copy old unfair choices (c) test on many groups (d) fix wrong labels
  26. 26.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) ignoring mistakes (d) more varied data
  27. 27.True or false: “check who is missing” adds to bias. (a) False (b) True
  28. 28.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  29. 29.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  30. 30.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  31. 31.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  32. 32.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  33. 33.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  34. 34.🔍 Which is the odd one out? (a) test on one group only (b) add more of the same (c) copy old unfair choices (d) balance the data
  35. 35.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  36. 36.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  37. 37.🔍 Which is the odd one out? (a) add varied examples (b) check who is missing (c) ask different people (d) skip testing
  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.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) add more of the same (d) test on many groups
  40. 40.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data

Answer key

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

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