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AI Bias Worksheet for Kids

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

Answer key

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

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