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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 941 · 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: “balance the data” adds to bias. (a) True (b) False
  2. 2.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  3. 3.Which of these adds to bias? (a) balance the data (b) test on many groups (c) check who is missing (d) skip testing
  4. 4.True or false: “test on many groups” adds to bias. (a) False (b) True
  5. 5.Which of these adds to bias? (a) add varied examples (b) ignore complaints (c) test on many groups (d) balance the data
  6. 6.🔍 Which is the odd one out? (a) fix wrong labels (b) copy old unfair choices (c) test on many groups (d) add varied examples
  7. 7.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  8. 8.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  9. 9.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  10. 10.True or false: “ask different people” helps reduce bias. (a) False (b) True
  11. 11.🔍 Which is the odd one out? (a) fix wrong labels (b) ignore complaints (c) ask different people (d) balance the data
  12. 12.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  13. 13.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) fewer tests (d) more varied data
  14. 14.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  15. 15.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  16. 16.🔍 Which is the odd one out? (a) check who is missing (b) balance the data (c) ask different people (d) use one group only
  17. 17.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
  18. 18.🔍 Which is the odd one out? (a) test on one group only (b) check who is missing (c) test on many groups (d) fix wrong labels
  19. 19.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  20. 20.🔍 Which is the odd one out? (a) copy old unfair choices (b) add more of the same (c) skip testing (d) add varied examples
  21. 21.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) add more of the same (d) balance the data
  22. 22.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  23. 23.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  24. 24.True or false: “add varied examples” adds to bias. (a) True (b) False
  25. 25.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a fair model (d) a broken screen
  26. 26.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  27. 27.Which of these helps reduce bias? (a) copy old unfair choices (b) add varied examples (c) use one group only (d) test on one group only
  28. 28.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) test on many groups (d) add more of the same
  29. 29.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) False (b) True
  30. 30.True or false: “skip testing” adds to bias. (a) False (b) True
  31. 31.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  32. 32.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  33. 33.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
  34. 34.🗣️ 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
  35. 35.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
  36. 36.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  37. 37.Which of these adds to bias? (a) test on one group only (b) balance the data (c) fix wrong labels (d) test on many groups
  38. 38.Which of these helps reduce bias? (a) check who is missing (b) copy old unfair choices (c) skip testing (d) ignore complaints
  39. 39.🔍 Which is the odd one out? (a) ignore complaints (b) add more of the same (c) copy old unfair choices (d) balance the data
  40. 40.🔍 Which is the odd one out? (a) check who is missing (b) add more of the same (c) use one group only (d) ignore complaints

Answer key

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

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