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AI Bias Worksheet for Kids

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

Answer key

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

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