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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 556 · 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.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  2. 2.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) balance the data (d) use one group only
  3. 3.🔍 Which is the odd one out? (a) test on one group only (b) copy old unfair choices (c) use one group only (d) add varied examples
  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 of these helps reduce bias? (a) test on many groups (b) test on one group only (c) add more of the same (d) use one group only
  6. 6.🔍 Which is the odd one out? (a) add varied examples (b) fix wrong labels (c) use one group only (d) test on many groups
  7. 7.True or false: “check who is missing” adds to bias. (a) False (b) True
  8. 8.Which of these helps reduce bias? (a) use one group only (b) ignore complaints (c) add varied examples (d) add more of the same
  9. 9.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  10. 10.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  11. 11.🔍 Which is the odd one out? (a) ignore complaints (b) fix wrong labels (c) use one group only (d) copy old unfair choices
  12. 12.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
  13. 13.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  14. 14.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) ignoring mistakes (d) more varied data
  15. 15.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  16. 16.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  17. 17.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  18. 18.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a broken screen (c) bias from data (d) a strong battery
  19. 19.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  20. 20.Which of these adds to bias? (a) test on many groups (b) copy old unfair choices (c) check who is missing (d) fix wrong labels
  21. 21.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  22. 22.True or false: “test on many groups” adds to bias. (a) False (b) True
  23. 23.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  24. 24.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  25. 25.🔍 Which is the odd one out? (a) fix wrong labels (b) add varied examples (c) ignore complaints (d) ask different people
  26. 26.🔍 Which is the odd one out? (a) check who is missing (b) test on many groups (c) ask different people (d) copy old unfair choices
  27. 27.Which of these adds to bias? (a) use one group only (b) test on many groups (c) ask different people (d) check who is missing
  28. 28.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  29. 29.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
  30. 30.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  31. 31.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  32. 32.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  33. 33.True or false: “balance the data” adds to bias. (a) True (b) False
  34. 34.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  35. 35.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  36. 36.True or false: “add varied examples” adds to bias. (a) True (b) False
  37. 37.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
  38. 38.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  39. 39.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  40. 40.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data

Answer key

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

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