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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 223 · 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.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) bias from data (d) a broken screen
  2. 2.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
  3. 3.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) few child voices in data (d) it was a rainy day
  4. 4.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  5. 5.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  6. 6.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  7. 7.🔍 Which is the odd one out? (a) fix wrong labels (b) add more of the same (c) skip testing (d) ignore complaints
  8. 8.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  9. 9.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  10. 10.🔍 Which is the odd one out? (a) fix wrong labels (b) add varied examples (c) copy old unfair choices (d) balance the data
  11. 11.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  12. 12.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  13. 13.Which of these adds to bias? (a) use one group only (b) add varied examples (c) ask different people (d) check who is missing
  14. 14.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  15. 15.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  16. 16.Which of these helps reduce bias? (a) add varied examples (b) copy old unfair choices (c) skip testing (d) test on one group only
  17. 17.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  18. 18.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  19. 19.🔍 Which is the odd one out? (a) test on many groups (b) use one group only (c) fix wrong labels (d) add varied examples
  20. 20.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  21. 21.Which of these adds to bias? (a) ask different people (b) copy old unfair choices (c) add varied examples (d) check who is missing
  22. 22.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  23. 23.🔍 Which is the odd one out? (a) test on one group only (b) use one group only (c) test on many groups (d) add more of the same
  24. 24.True or false: “skip testing” adds to bias. (a) True (b) False
  25. 25.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  26. 26.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  27. 27.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  28. 28.Which of these adds to bias? (a) check who is missing (b) test on one group only (c) test on many groups (d) fix wrong labels
  29. 29.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  30. 30.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  31. 31.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  32. 32.True or false: “add varied examples” adds to bias. (a) True (b) False
  33. 33.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  34. 34.True or false: “balance the data” adds to bias. (a) True (b) False
  35. 35.🔍 Which is the odd one out? (a) balance the data (b) fix wrong labels (c) ignore complaints (d) ask different people
  36. 36.🛠️ What helps make an AI system fairer? (a) one group only (b) fewer tests (c) more varied data (d) ignoring mistakes
  37. 37.Which of these helps reduce bias? (a) test on many groups (b) add more of the same (c) ignore complaints (d) use one group only
  38. 38.True or false: “ask different people” helps reduce bias. (a) True (b) False
  39. 39.True or false: “add more of the same” helps reduce bias. (a) False (b) True
  40. 40.True or false: “ask different people” adds to bias. (a) True (b) False

Answer key

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

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