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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 621 · 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 handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  2. 2.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) few child voices in data (d) the phone was new
  3. 3.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  4. 4.🔍 Which is the odd one out? (a) test on many groups (b) add more of the same (c) ask different people (d) add varied examples
  5. 5.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  6. 6.Which of these adds to bias? (a) check who is missing (b) ignore complaints (c) fix wrong labels (d) balance the data
  7. 7.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  8. 8.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) one group only (d) more varied data
  9. 9.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its training data (d) its screen size
  10. 10.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  11. 11.🔍 Which is the odd one out? (a) use one group only (b) check who is missing (c) skip testing (d) add more of the same
  12. 12.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  13. 13.True or false: “ask different people” helps reduce bias. (a) True (b) False
  14. 14.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) use one group only (d) check who is missing
  15. 15.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  16. 16.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  17. 17.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  18. 18.🔍 Which is the odd one out? (a) fix wrong labels (b) add varied examples (c) skip testing (d) test on many groups
  19. 19.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) test on one group only (d) balance the data
  20. 20.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  21. 21.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  23. 23.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  24. 24.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  25. 25.Which of these helps reduce bias? (a) add more of the same (b) test on many groups (c) copy old unfair choices (d) ignore complaints
  26. 26.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  27. 27.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  28. 28.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  29. 29.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  30. 30.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  31. 31.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) test on one group only (d) add varied examples
  32. 32.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  33. 33.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  34. 34.Which of these adds to bias? (a) fix wrong labels (b) skip testing (c) add varied examples (d) check who is missing
  35. 35.True or false: “skip testing” helps reduce bias. (a) False (b) True
  36. 36.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  37. 37.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  38. 38.🔍 Which is the odd one out? (a) add more of the same (b) copy old unfair choices (c) test on many groups (d) test on one group only
  39. 39.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  40. 40.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) True (b) False

Answer key

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

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