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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 242 · 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: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  2. 2.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  3. 3.True or false: “test on many groups” adds to bias. (a) True (b) False
  4. 4.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  5. 5.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  6. 6.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  7. 7.🔍 Which is the odd one out? (a) balance the data (b) fix wrong labels (c) check who is missing (d) skip testing
  8. 8.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  9. 9.Which of these helps reduce bias? (a) add more of the same (b) copy old unfair choices (c) balance the data (d) test on one group only
  10. 10.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  11. 11.True or false: “check who is missing” adds to bias. (a) True (b) False
  12. 12.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  13. 13.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) fewer tests (d) more varied data
  14. 14.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  15. 15.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its screen size (d) its colour
  16. 16.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  17. 17.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  18. 18.🔍 Which is the odd one out? (a) add more of the same (b) test on one group only (c) ignore complaints (d) balance the data
  19. 19.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  20. 20.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  21. 21.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  22. 22.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  23. 23.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  24. 24.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  25. 25.Which of these helps reduce bias? (a) ask different people (b) copy old unfair choices (c) add more of the same (d) use one group only
  26. 26.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
  27. 27.🗣️ 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) few child voices in data (d) the phone was new
  28. 28.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  29. 29.Which of these adds to bias? (a) add varied examples (b) check who is missing (c) add more of the same (d) test on many groups
  30. 30.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  31. 31.Which of these helps reduce bias? (a) use one group only (b) ignore complaints (c) fix wrong labels (d) copy old unfair choices
  32. 32.Which of these helps reduce bias? (a) add varied examples (b) copy old unfair choices (c) test on one group only (d) ignore complaints
  33. 33.🔍 Which is the odd one out? (a) add varied examples (b) skip testing (c) use one group only (d) test on one group only
  34. 34.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) use one group only (d) test on many groups
  35. 35.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
  36. 36.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  37. 37.🔍 Which is the odd one out? (a) ignore complaints (b) test on one group only (c) use one group only (d) ask different people
  38. 38.Which of these adds to bias? (a) check who is missing (b) test on many groups (c) add varied examples (d) ignore complaints
  39. 39.True or false: “balance the data” helps reduce bias. (a) False (b) True
  40. 40.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data

Answer key

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

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