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AI Bias Worksheet for Kids

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

Answer key

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

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