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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 644 · 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.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  2. 2.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
  3. 3.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  4. 4.Which of these adds to bias? (a) balance the data (b) fix wrong labels (c) copy old unfair choices (d) test on many groups
  5. 5.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  6. 6.Which of these adds to bias? (a) test on many groups (b) use one group only (c) balance the data (d) check who is missing
  7. 7.🗣️ 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
  8. 8.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  9. 9.True or false: “use one group only” helps reduce bias. (a) True (b) False
  10. 10.🔍 Which is the odd one out? (a) add varied examples (b) ignore complaints (c) balance the data (d) ask different people
  11. 11.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  12. 12.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
  13. 13.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  14. 14.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) False (b) True
  15. 15.🔍 Which is the odd one out? (a) fix wrong labels (b) skip testing (c) copy old unfair choices (d) test on one group only
  16. 16.🛠️ What helps make an AI system fairer? (a) fewer tests (b) ignoring mistakes (c) more varied data (d) one group only
  17. 17.True or false: “add more of the same” adds to bias. (a) False (b) True
  18. 18.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) True (b) False
  19. 19.Which of these adds to bias? (a) add varied examples (b) skip testing (c) balance the data (d) fix wrong labels
  20. 20.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  21. 21.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) True (b) False
  22. 22.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
  23. 23.True or false: “fix wrong labels” adds to bias. (a) False (b) True
  24. 24.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) False (b) True
  25. 25.🛠️ Does this reduce bias or add to it: add more of the same? (a) adds to bias (b) reduces bias
  26. 26.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  27. 27.🔍 Which is the odd one out? (a) skip testing (b) add varied examples (c) balance the data (d) check who is missing
  28. 28.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  29. 29.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  30. 30.Which of these adds to bias? (a) test on many groups (b) balance the data (c) add varied examples (d) test on one group only
  31. 31.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  32. 32.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  33. 33.🔍 Which is the odd one out? (a) check who is missing (b) ask different people (c) add varied examples (d) use one group only
  34. 34.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  35. 35.🔍 Which is the odd one out? (a) skip testing (b) copy old unfair choices (c) use one group only (d) add varied examples
  36. 36.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  37. 37.True or false: “test on one group only” adds to bias. (a) True (b) False
  38. 38.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  39. 39.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  40. 40.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data

Answer key

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

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