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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 105 · 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.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  2. 2.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) ignoring mistakes (d) one group only
  3. 3.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  4. 4.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) add more of the same (d) add varied examples
  5. 5.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  6. 6.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  7. 7.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) children speak too fast (c) it was a rainy day (d) the phone was new
  8. 8.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
  9. 9.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  10. 10.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  11. 11.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  12. 12.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  13. 13.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  14. 14.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  15. 15.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  16. 16.🖼️ 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
  17. 17.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  18. 18.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  19. 19.True or false: “ask different people” adds to bias. (a) True (b) False
  20. 20.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  21. 21.Which of these helps reduce bias? (a) add more of the same (b) test on one group only (c) skip testing (d) ask different people
  22. 22.Which of these adds to bias? (a) add more of the same (b) ask different people (c) balance the data (d) check who is missing
  23. 23.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  24. 24.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  25. 25.True or false: “add more of the same” adds to bias. (a) False (b) True
  26. 26.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
  27. 27.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  28. 28.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  29. 29.Which of these helps reduce bias? (a) copy old unfair choices (b) skip testing (c) fix wrong labels (d) add more of the same
  30. 30.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  31. 31.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  32. 32.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
  33. 33.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  34. 34.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) True (b) False
  35. 35.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  36. 36.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  37. 37.True or false: “check who is missing” adds to bias. (a) True (b) False
  38. 38.Which of these helps reduce bias? (a) copy old unfair choices (b) balance the data (c) use one group only (d) ignore complaints
  39. 39.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  40. 40.True or false: “ignore complaints” adds to bias. (a) True (b) False

Answer key

  1. True
  2. more varied data
  3. False
  4. add more of the same
  5. True
  6. one-sided data
  7. few child voices in data
  8. True
  9. True
  10. False
  11. True
  12. False
  13. one-sided data
  14. True
  15. adds to bias
  16. bias from data
  17. False
  18. adds to bias
  19. False
  20. balanced data
  21. ask different people
  22. add more of the same
  23. True
  24. True
  25. True
  26. False
  27. balanced data
  28. one-sided data
  29. fix wrong labels
  30. True
  31. False
  32. True
  33. adds to bias
  34. True
  35. adds to bias
  36. False
  37. False
  38. balance the data
  39. reduces bias
  40. True

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