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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 619 · 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: “add varied examples” adds to bias. (a) True (b) False
  2. 2.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  3. 3.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  4. 4.True or false: “ask different people” helps reduce bias. (a) True (b) False
  5. 5.True or false: “balance the data” adds to bias. (a) False (b) True
  6. 6.🔍 Which is the odd one out? (a) ask different people (b) fix wrong labels (c) use one group only (d) check who is missing
  7. 7.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  8. 8.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  9. 9.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  10. 10.🔍 Which is the odd one out? (a) ignore complaints (b) skip testing (c) test on one group only (d) add varied examples
  11. 11.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) test on many groups (d) copy old unfair choices
  12. 12.🔍 Which is the odd one out? (a) skip testing (b) test on many groups (c) add more of the same (d) use one group only
  13. 13.Which of these helps reduce bias? (a) skip testing (b) test on one group only (c) balance the data (d) use one group only
  14. 14.Which of these helps reduce bias? (a) test on many groups (b) test on one group only (c) skip testing (d) use one group only
  15. 15.Which of these adds to bias? (a) check who is missing (b) test on many groups (c) add varied examples (d) skip testing
  16. 16.🔍 Which is the odd one out? (a) use one group only (b) copy old unfair choices (c) test on one group only (d) fix wrong labels
  17. 17.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) few child voices in data (c) the phone was new (d) it was a rainy day
  18. 18.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  19. 19.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  20. 20.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  21. 21.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  22. 22.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  23. 23.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) use one group only (d) ask different people
  24. 24.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
  25. 25.True or false: “use one group only” adds to bias. (a) True (b) False
  26. 26.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  27. 27.Which of these helps reduce bias? (a) skip testing (b) add more of the same (c) fix wrong labels (d) test on one group only
  28. 28.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) more varied data (d) one group only
  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: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  31. 31.True or false: “check who is missing” adds to bias. (a) True (b) False
  32. 32.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) bias from data (c) a strong battery (d) a fair model
  33. 33.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  34. 34.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  35. 35.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  36. 36.🔍 Which is the odd one out? (a) copy old unfair choices (b) add more of the same (c) ignore complaints (d) balance the data
  37. 37.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  38. 38.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
  39. 39.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  40. 40.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True

Answer key

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

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