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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 733 · 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: “check who is missing” helps reduce bias. (a) False (b) True
  2. 2.Which of these helps reduce bias? (a) add more of the same (b) use one group only (c) balance the data (d) skip testing
  3. 3.True or false: “use one group only” helps reduce bias. (a) False (b) True
  4. 4.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) few child voices in data (c) children speak too fast (d) the phone was new
  5. 5.Which of these helps reduce bias? (a) use one group only (b) fix wrong labels (c) add more of the same (d) skip testing
  6. 6.🔍 Which is the odd one out? (a) skip testing (b) balance the data (c) ask different people (d) test on many groups
  7. 7.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  8. 8.Which of these helps reduce bias? (a) use one group only (b) test on many groups (c) ignore complaints (d) copy old unfair choices
  9. 9.True or false: “ask different people” helps reduce bias. (a) False (b) True
  10. 10.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  11. 11.🔍 Which is the odd one out? (a) check who is missing (b) balance the data (c) ask different people (d) add more of the same
  12. 12.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  13. 13.True or false: “add more of the same” adds to bias. (a) True (b) False
  14. 14.Which of these adds to bias? (a) check who is missing (b) use one group only (c) balance the data (d) ask different people
  15. 15.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  16. 16.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  17. 17.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  18. 18.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  19. 19.🔍 Which is the odd one out? (a) balance the data (b) copy old unfair choices (c) add varied examples (d) check who is missing
  20. 20.🔍 Which is the odd one out? (a) balance the data (b) use one group only (c) add varied examples (d) ask different people
  21. 21.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  22. 22.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
  23. 23.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  24. 24.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  25. 25.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  26. 26.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  27. 27.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) ignore complaints (d) fix wrong labels
  28. 28.True or false: leaves from every season of the year is balanced data (fairer for everyone). (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.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  31. 31.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
  32. 32.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  33. 33.Which of these helps reduce bias? (a) skip testing (b) add varied examples (c) test on one group only (d) use one group only
  34. 34.Which of these helps reduce bias? (a) copy old unfair choices (b) add more of the same (c) check who is missing (d) ignore complaints
  35. 35.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  36. 36.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  37. 37.Which of these adds to bias? (a) copy old unfair choices (b) test on many groups (c) check who is missing (d) balance the data
  38. 38.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) fewer tests (c) more varied data (d) one group only
  39. 39.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a fair model (b) a broken screen (c) a strong battery (d) bias from data
  40. 40.True or false: “add more of the same” helps reduce bias. (a) False (b) True

Answer key

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

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