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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 670 · 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.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) ignoring mistakes (d) one group only
  2. 2.True or false: “test on many groups” adds to bias. (a) False (b) True
  3. 3.🔍 Which is the odd one out? (a) copy old unfair choices (b) balance the data (c) test on many groups (d) check who is missing
  4. 4.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) check who is missing (d) add more of the same
  5. 5.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a strong battery (c) a fair model (d) bias from data
  6. 6.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  7. 7.True or false: “ask different people” helps reduce bias. (a) True (b) False
  8. 8.🔍 Which is the odd one out? (a) add varied examples (b) test on many groups (c) ask different people (d) add more of the same
  9. 9.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its screen size (d) its training data
  10. 10.True or false: “check who is missing” adds to bias. (a) True (b) False
  11. 11.True or false: “add more of the same” helps reduce bias. (a) False (b) True
  12. 12.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  13. 13.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  14. 14.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  15. 15.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  16. 16.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) add more of the same (d) add varied examples
  17. 17.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  18. 18.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  19. 19.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  20. 20.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) fix wrong labels (d) skip testing
  21. 21.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  22. 22.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  23. 23.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  24. 24.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
  25. 25.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
  26. 26.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  27. 27.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  28. 28.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) children speak too fast (d) few child voices in data
  29. 29.Which of these helps reduce bias? (a) add more of the same (b) skip testing (c) use one group only (d) ask different people
  30. 30.Which of these adds to bias? (a) ask different people (b) balance the data (c) add more of the same (d) check who is missing
  31. 31.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  32. 32.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) add varied examples (d) skip testing
  33. 33.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  34. 34.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  35. 35.Which of these adds to bias? (a) ignore complaints (b) check who is missing (c) ask different people (d) add varied examples
  36. 36.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  37. 37.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  38. 38.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  39. 39.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  40. 40.True or false: “add varied examples” helps reduce bias. (a) True (b) False

Answer key

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

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