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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 659 · 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.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  2. 2.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) a strong battery (d) bias from data
  3. 3.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  4. 4.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  5. 5.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
  6. 6.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) more varied data (d) fewer tests
  7. 7.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  8. 8.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  9. 9.True or false: “balance the data” adds to bias. (a) True (b) False
  10. 10.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  11. 11.Which of these helps reduce bias? (a) add more of the same (b) test on one group only (c) balance the data (d) ignore complaints
  12. 12.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  13. 13.True or false: “ask different people” adds to bias. (a) True (b) False
  14. 14.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  15. 15.True or false: “skip testing” helps reduce bias. (a) False (b) True
  16. 16.🔍 Which is the odd one out? (a) fix wrong labels (b) balance the data (c) ask different people (d) copy old unfair choices
  17. 17.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  18. 18.🔍 Which is the odd one out? (a) copy old unfair choices (b) ignore complaints (c) check who is missing (d) add more of the same
  19. 19.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
  20. 20.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  21. 21.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  22. 22.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  23. 23.Which of these helps reduce bias? (a) use one group only (b) add more of the same (c) test on many groups (d) skip testing
  24. 24.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
  25. 25.🔍 Which is the odd one out? (a) add varied examples (b) ignore complaints (c) test on many groups (d) check who is missing
  26. 26.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  27. 27.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  28. 28.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  29. 29.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  30. 30.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) use one group only (d) balance the data
  31. 31.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  32. 32.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  33. 33.True or false: “ignore complaints” adds to bias. (a) True (b) False
  34. 34.Which of these adds to bias? (a) copy old unfair choices (b) balance the data (c) fix wrong labels (d) test on many groups
  35. 35.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) use one group only (d) copy old unfair choices
  36. 36.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  37. 37.Which of these helps reduce bias? (a) ignore complaints (b) fix wrong labels (c) use one group only (d) add more of the same
  38. 38.Which of these adds to bias? (a) skip testing (b) add varied examples (c) check who is missing (d) fix wrong labels
  39. 39.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
  40. 40.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its colour (d) its screen size

Answer key

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

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