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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 24 · 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.🔍 Which is the odd one out? (a) balance the data (b) add varied examples (c) ask different people (d) add more of the same
  2. 2.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) False (b) True
  3. 3.🔍 Which is the odd one out? (a) balance the data (b) fix wrong labels (c) test on many groups (d) skip testing
  4. 4.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  5. 5.Which of these helps reduce bias? (a) ask different people (b) skip testing (c) copy old unfair choices (d) add more of the same
  6. 6.Which of these adds to bias? (a) skip testing (b) fix wrong labels (c) ask different people (d) add varied examples
  7. 7.Which of these helps reduce bias? (a) test on one group only (b) skip testing (c) fix wrong labels (d) add more of the same
  8. 8.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  9. 9.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  10. 10.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  11. 11.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  12. 12.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  13. 13.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  14. 14.True or false: “add more of the same” adds to bias. (a) True (b) False
  15. 15.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  16. 16.Which of these adds to bias? (a) fix wrong labels (b) copy old unfair choices (c) check who is missing (d) test on many groups
  17. 17.True or false: “ignore complaints” adds to bias. (a) True (b) False
  18. 18.Which of these adds to bias? (a) check who is missing (b) ask different people (c) use one group only (d) balance the data
  19. 19.True or false: “balance the data” adds to bias. (a) False (b) True
  20. 20.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) more varied data (d) fewer tests
  21. 21.🗣️ 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) few child voices in data (d) children speak too fast
  22. 22.True or false: “add varied examples” adds to bias. (a) True (b) False
  23. 23.Which of these adds to bias? (a) add varied examples (b) check who is missing (c) ignore complaints (d) test on many groups
  24. 24.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  25. 25.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  26. 26.Which of these helps reduce bias? (a) skip testing (b) add more of the same (c) test on many groups (d) use one group only
  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) False (b) True
  28. 28.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  29. 29.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  30. 30.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
  31. 31.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a broken screen (d) a fair model
  32. 32.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  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: test on many groups? (a) adds to bias (b) reduces bias
  35. 35.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
  36. 36.🔍 Which is the odd one out? (a) test on one group only (b) ignore complaints (c) copy old unfair choices (d) ask different people
  37. 37.🔍 Which is the odd one out? (a) copy old unfair choices (b) check who is missing (c) use one group only (d) add more of the same
  38. 38.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  39. 39.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  40. 40.True or false: “test on many groups” adds to bias. (a) False (b) True

Answer key

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

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