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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 42 · 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: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  2. 2.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  3. 3.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
  4. 4.Which of these helps reduce bias? (a) use one group only (b) skip testing (c) add varied examples (d) ignore complaints
  5. 5.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) the phone was new (c) children speak too fast (d) it was a rainy day
  6. 6.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  7. 7.True or false: “add varied examples” adds to bias. (a) False (b) True
  8. 8.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
  9. 9.Which of these helps reduce bias? (a) skip testing (b) copy old unfair choices (c) fix wrong labels (d) ignore complaints
  10. 10.🔍 Which is the odd one out? (a) skip testing (b) ignore complaints (c) use one group only (d) balance the data
  11. 11.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
  12. 12.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  13. 13.True or false: “test on many groups” adds to bias. (a) False (b) True
  14. 14.Which of these helps reduce bias? (a) copy old unfair choices (b) use one group only (c) skip testing (d) balance the data
  15. 15.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  16. 16.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) its screen size (d) the weather
  17. 17.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  18. 18.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  19. 19.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  20. 20.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  21. 21.🔍 Which is the odd one out? (a) ask different people (b) fix wrong labels (c) check who is missing (d) test on one group only
  22. 22.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  23. 23.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  24. 24.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  25. 25.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  26. 26.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  27. 27.Which of these adds to bias? (a) test on many groups (b) add varied examples (c) fix wrong labels (d) add more of the same
  28. 28.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  29. 29.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
  30. 30.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  31. 31.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  32. 32.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  33. 33.Which of these adds to bias? (a) test on many groups (b) skip testing (c) balance the data (d) check who is missing
  34. 34.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  35. 35.True or false: “ask different people” adds to bias. (a) True (b) False
  36. 36.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  37. 37.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) fewer tests (d) more varied data
  38. 38.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  39. 39.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  40. 40.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data

Answer key

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

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