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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 868 · 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) check who is missing (b) copy old unfair choices (c) add more of the same (d) test on one group only
  2. 2.True or false: “add varied examples” adds to bias. (a) False (b) True
  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 varied examples? (a) adds to bias (b) reduces bias
  5. 5.True or false: “use one group only” adds to bias. (a) True (b) False
  6. 6.True or false: “ignore complaints” adds to bias. (a) False (b) True
  7. 7.Which of these helps reduce bias? (a) balance the data (b) skip testing (c) test on one group only (d) copy old unfair choices
  8. 8.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
  9. 9.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  10. 10.Which of these helps reduce bias? (a) copy old unfair choices (b) ask different people (c) skip testing (d) ignore complaints
  11. 11.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) test on many groups (d) copy old unfair choices
  12. 12.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  13. 13.Which of these helps reduce bias? (a) add varied examples (b) copy old unfair choices (c) use one group only (d) skip testing
  14. 14.Which of these adds to bias? (a) balance the data (b) check who is missing (c) copy old unfair choices (d) fix wrong labels
  15. 15.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  16. 16.Which of these helps reduce bias? (a) check who is missing (b) ignore complaints (c) copy old unfair choices (d) add more of the same
  17. 17.True or false: “ask different people” helps reduce bias. (a) False (b) True
  18. 18.True or false: “balance the data” helps reduce bias. (a) True (b) False
  19. 19.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
  20. 20.🔍 Which is the odd one out? (a) skip testing (b) copy old unfair choices (c) test on many groups (d) use one group only
  21. 21.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) balance the data (d) add more of the same
  22. 22.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
  23. 23.Which of these adds to bias? (a) test on many groups (b) add more of the same (c) add varied examples (d) ask different people
  24. 24.🔍 Which is the odd one out? (a) use one group only (b) fix wrong labels (c) test on many groups (d) ask different people
  25. 25.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False
  26. 26.🔍 Which is the odd one out? (a) balance the data (b) ignore complaints (c) add varied examples (d) check who is missing
  27. 27.🔍 Which is the odd one out? (a) test on one group only (b) use one group only (c) ignore complaints (d) add varied examples
  28. 28.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  29. 29.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) the weather (d) its screen size
  30. 30.🔍 Which is the odd one out? (a) fix wrong labels (b) test on many groups (c) skip testing (d) check who is missing
  31. 31.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a fair model (c) a strong battery (d) a broken screen
  32. 32.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  33. 33.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  34. 34.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  35. 35.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  36. 36.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  37. 37.True or false: “use one group only” helps reduce bias. (a) False (b) True
  38. 38.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  39. 39.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  40. 40.Which of these adds to bias? (a) test on many groups (b) balance the data (c) skip testing (d) check who is missing

Answer key

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

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