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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 393 · 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: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  2. 2.Which of these helps reduce bias? (a) ignore complaints (b) copy old unfair choices (c) skip testing (d) ask different people
  3. 3.🛠️ What helps make an AI system fairer? (a) more varied data (b) ignoring mistakes (c) one group only (d) fewer tests
  4. 4.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) balance the data (d) ignore complaints
  5. 5.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  6. 6.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  7. 7.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  8. 8.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  9. 9.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  10. 10.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) add varied examples (d) copy old unfair choices
  11. 11.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  12. 12.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) False (b) True
  13. 13.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
  14. 14.True or false: “skip testing” adds to bias. (a) True (b) False
  15. 15.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  16. 16.Which of these adds to bias? (a) skip testing (b) check who is missing (c) fix wrong labels (d) add varied examples
  17. 17.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  18. 18.🔍 Which is the odd one out? (a) check who is missing (b) copy old unfair choices (c) use one group only (d) ignore complaints
  19. 19.Which of these adds to bias? (a) test on many groups (b) ask different people (c) add varied examples (d) test on one group only
  20. 20.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  21. 21.True or false: “test on many groups” adds to bias. (a) True (b) False
  22. 22.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  23. 23.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  24. 24.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) the phone was new (c) few child voices in data (d) children speak too fast
  25. 25.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  26. 26.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  27. 27.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  28. 28.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  29. 29.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
  30. 30.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) ignore complaints (d) check who is missing
  31. 31.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  32. 32.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  33. 33.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  34. 34.True or false: “use one group only” helps reduce bias. (a) True (b) False
  35. 35.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  36. 36.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  37. 37.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  38. 38.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  39. 39.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
  40. 40.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False

Answer key

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

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