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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 28 · 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 is the odd one out? (a) check who is missing (b) add varied examples (c) balance the data (d) use one group only
  2. 2.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  3. 3.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  4. 4.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  5. 5.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  6. 6.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  7. 7.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) use one group only (d) ask different people
  8. 8.Which of these helps reduce bias? (a) ignore complaints (b) check who is missing (c) test on one group only (d) skip testing
  9. 9.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  10. 10.🛠️ What helps make an AI system fairer? (a) more varied data (b) fewer tests (c) ignoring mistakes (d) one group only
  11. 11.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  12. 12.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  13. 13.🔍 Which is the odd one out? (a) test on one group only (b) use one group only (c) check who is missing (d) add more of the same
  14. 14.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  15. 15.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  16. 16.Which of these adds to bias? (a) test on many groups (b) add varied examples (c) ask different people (d) skip testing
  17. 17.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  18. 18.Which of these adds to bias? (a) fix wrong labels (b) test on one group only (c) test on many groups (d) add varied examples
  19. 19.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
  20. 20.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  21. 21.🗣️ 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
  22. 22.True or false: “balance the data” adds to bias. (a) False (b) True
  23. 23.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  24. 24.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  25. 25.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  26. 26.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a strong battery (c) bias from data (d) a fair model
  27. 27.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  28. 28.🔍 Which is the odd one out? (a) test on one group only (b) add more of the same (c) balance the data (d) use one group only
  29. 29.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) the weather (d) its colour
  30. 30.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  31. 31.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  32. 32.True or false: “test on many groups” adds to bias. (a) True (b) False
  33. 33.🛠️ Does this reduce bias or add to it: copy old unfair choices? (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.🔍 Which is the odd one out? (a) balance the data (b) skip testing (c) fix wrong labels (d) test on many groups
  36. 36.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  37. 37.True or false: “balance the data” helps reduce bias. (a) False (b) True
  38. 38.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  39. 39.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  40. 40.Which of these adds to bias? (a) check who is missing (b) copy old unfair choices (c) ask different people (d) test on many groups

Answer key

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

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