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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 135 · 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 more of the same (b) add varied examples (c) ignore complaints (d) use one group only
  2. 2.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  3. 3.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  4. 4.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  5. 5.Which of these helps reduce bias? (a) test on many groups (b) skip testing (c) use one group only (d) copy old unfair choices
  6. 6.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) the weather (d) its training data
  7. 7.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  8. 8.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  9. 9.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a broken screen (c) bias from data (d) a fair model
  10. 10.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  11. 11.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  12. 12.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  13. 13.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  14. 14.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  15. 15.Which of these adds to bias? (a) fix wrong labels (b) check who is missing (c) skip testing (d) add varied examples
  16. 16.True or false: “check who is missing” adds to bias. (a) True (b) False
  17. 17.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  18. 18.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
  19. 19.True or false: “skip testing” helps reduce bias. (a) False (b) True
  20. 20.🔍 Which is the odd one out? (a) balance the data (b) ignore complaints (c) fix wrong labels (d) test on many groups
  21. 21.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  22. 22.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  23. 23.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) skip testing (d) fix wrong labels
  24. 24.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) fewer tests (c) more varied data (d) one group only
  25. 25.🔍 Which is the odd one out? (a) ignore complaints (b) skip testing (c) use one group only (d) ask different people
  26. 26.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  27. 27.🔍 Which is the odd one out? (a) use one group only (b) test on one group only (c) add more of the same (d) test on many groups
  28. 28.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  29. 29.🔍 Which is the odd one out? (a) use one group only (b) test on one group only (c) ignore complaints (d) add varied examples
  30. 30.Which of these adds to bias? (a) ask different people (b) fix wrong labels (c) ignore complaints (d) balance the data
  31. 31.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  32. 32.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  33. 33.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  34. 34.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  35. 35.True or false: “balance the data” helps reduce bias. (a) True (b) False
  36. 36.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  37. 37.Which of these helps reduce bias? (a) copy old unfair choices (b) balance the data (c) test on one group only (d) use one group only
  38. 38.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  39. 39.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  40. 40.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data

Answer key

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

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