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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 563 · 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.Which of these helps reduce bias? (a) copy old unfair choices (b) add varied examples (c) use one group only (d) ignore complaints
  2. 2.🔍 Which is the odd one out? (a) fix wrong labels (b) check who is missing (c) test on many groups (d) use one group only
  3. 3.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  4. 4.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  5. 5.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  6. 6.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  7. 7.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its screen size (d) its colour
  8. 8.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  9. 9.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  10. 10.Which of these adds to bias? (a) check who is missing (b) add more of the same (c) ask different people (d) test on many groups
  11. 11.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  12. 12.True or false: “check who is missing” adds to bias. (a) False (b) True
  13. 13.True or false: “ask different people” adds to bias. (a) False (b) True
  14. 14.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  15. 15.🔍 Which is the odd one out? (a) add more of the same (b) copy old unfair choices (c) ignore complaints (d) check who is missing
  16. 16.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  17. 17.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  18. 18.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a broken screen (c) a strong battery (d) a fair model
  19. 19.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) use one group only (d) fix wrong labels
  20. 20.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  21. 21.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) it was a rainy day (d) few child voices in data
  22. 22.Which of these helps reduce bias? (a) use one group only (b) add more of the same (c) skip testing (d) balance the data
  23. 23.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  24. 24.True or false: “skip testing” helps reduce bias. (a) True (b) False
  25. 25.🔍 Which is the odd one out? (a) test on one group only (b) copy old unfair choices (c) skip testing (d) fix wrong labels
  26. 26.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) more varied data (d) ignoring mistakes
  27. 27.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  28. 28.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  29. 29.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
  30. 30.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  31. 31.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  32. 32.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  33. 33.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  34. 34.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  35. 35.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  36. 36.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  37. 37.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) copy old unfair choices (d) test on many groups
  38. 38.True or false: “skip testing” adds to bias. (a) False (b) True
  39. 39.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  40. 40.Which of these adds to bias? (a) check who is missing (b) copy old unfair choices (c) balance the data (d) ask different people

Answer key

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

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