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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 441 · 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) copy old unfair choices (b) ask different people (c) add varied examples (d) check who is missing
  2. 2.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
  3. 3.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  4. 4.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  5. 5.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) few child voices in data (c) children speak too fast (d) the phone was new
  6. 6.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  7. 7.🔍 Which is the odd one out? (a) ask different people (b) balance the data (c) fix wrong labels (d) skip testing
  8. 8.Which of these helps reduce bias? (a) skip testing (b) use one group only (c) test on one group only (d) balance the data
  9. 9.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  10. 10.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) one group only (c) fewer tests (d) more varied data
  11. 11.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) test on one group only (d) ask different people
  12. 12.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  13. 13.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  14. 14.True or false: “ask different people” adds to bias. (a) False (b) True
  15. 15.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  16. 16.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  17. 17.True or false: “test on one group only” adds to bias. (a) False (b) True
  18. 18.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  19. 19.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  20. 20.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  21. 21.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
  22. 22.🔍 Which is the odd one out? (a) fix wrong labels (b) copy old unfair choices (c) check who is missing (d) ask different people
  23. 23.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  24. 24.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) its colour (d) the weather
  25. 25.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  26. 26.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  27. 27.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  28. 28.Which of these adds to bias? (a) add varied examples (b) balance the data (c) check who is missing (d) test on one group only
  29. 29.True or false: “balance the data” adds to bias. (a) True (b) False
  30. 30.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) add more of the same (d) check who is missing
  31. 31.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  32. 32.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  33. 33.True or false: “test on one group only” helps reduce bias. (a) False (b) True
  34. 34.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  35. 35.🔍 Which is the odd one out? (a) test on many groups (b) ask different people (c) use one group only (d) check who is missing
  36. 36.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
  37. 37.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  38. 38.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  39. 39.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  40. 40.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data

Answer key

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

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