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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 483 · 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) add varied examples (b) ask different people (c) use one group only (d) test on many groups
  2. 2.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  3. 3.🔍 Which is the odd one out? (a) check who is missing (b) skip testing (c) ignore complaints (d) use one group only
  4. 4.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  5. 5.🔍 Which is the odd one out? (a) add varied examples (b) check who is missing (c) fix wrong labels (d) add more of the same
  6. 6.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  7. 7.Which of these adds to bias? (a) ask different people (b) skip testing (c) test on many groups (d) check who is missing
  8. 8.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  9. 9.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  10. 10.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  11. 11.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  12. 12.True or false: “use one group only” helps reduce bias. (a) True (b) False
  13. 13.Which of these adds to bias? (a) add varied examples (b) fix wrong labels (c) balance the data (d) ignore complaints
  14. 14.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  15. 15.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  16. 16.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) fix wrong labels (d) use one group only
  17. 17.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) it was a rainy day (c) the phone was new (d) few child voices in data
  18. 18.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  19. 19.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  20. 20.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  21. 21.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) its colour (d) the weather
  22. 22.Which of these helps reduce bias? (a) copy old unfair choices (b) test on many groups (c) add more of the same (d) use one group only
  23. 23.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  24. 24.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a broken screen (d) a fair model
  25. 25.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  26. 26.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  27. 27.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  28. 28.🔍 Which is the odd one out? (a) copy old unfair choices (b) ignore complaints (c) add more of the same (d) ask different people
  29. 29.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  30. 30.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
  31. 31.True or false: “copy old unfair choices” helps reduce bias. (a) False (b) True
  32. 32.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  33. 33.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  34. 34.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) add more of the same (d) fix wrong labels
  35. 35.True or false: “add more of the same” adds to bias. (a) True (b) False
  36. 36.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  37. 37.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  38. 38.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) fewer tests (d) more varied data
  39. 39.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  40. 40.True or false: “add varied examples” adds to bias. (a) False (b) True

Answer key

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

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