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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 337 · 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: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
  2. 2.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  3. 3.True or false: “ask different people” helps reduce bias. (a) True (b) False
  4. 4.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  5. 5.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) the weather (d) its training data
  6. 6.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  7. 7.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  8. 8.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  9. 9.🔍 Which is the odd one out? (a) balance the data (b) use one group only (c) fix wrong labels (d) add varied examples
  10. 10.Which of these adds to bias? (a) fix wrong labels (b) check who is missing (c) copy old unfair choices (d) add varied examples
  11. 11.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  12. 12.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  13. 13.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  14. 14.Which of these helps reduce bias? (a) check who is missing (b) test on one group only (c) skip testing (d) use one group only
  15. 15.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) few child voices in data (d) the phone was new
  16. 16.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  17. 17.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  18. 18.🔍 Which is the odd one out? (a) test on one group only (b) check who is missing (c) balance the data (d) test on many groups
  19. 19.🖼️ 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
  20. 20.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  21. 21.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  23. 23.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  24. 24.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  25. 25.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  26. 26.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  27. 27.Which of these helps reduce bias? (a) use one group only (b) balance the data (c) copy old unfair choices (d) skip testing
  28. 28.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) reduces bias (b) adds to bias
  29. 29.Which of these adds to bias? (a) fix wrong labels (b) test on many groups (c) check who is missing (d) ignore complaints
  30. 30.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
  31. 31.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  32. 32.True or false: “ask different people” adds to bias. (a) True (b) False
  33. 33.True or false: “test on many groups” adds to bias. (a) True (b) False
  34. 34.True or false: “skip testing” adds to bias. (a) False (b) True
  35. 35.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  36. 36.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  37. 37.True or false: “test on one group only” adds to bias. (a) False (b) True
  38. 38.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  39. 39.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) fewer tests (c) more varied data (d) one group only
  40. 40.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) copy old unfair choices (d) add varied examples

Answer key

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

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