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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 147 · 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: use one group only? (a) reduces bias (b) adds to bias
  2. 2.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) one group only (d) fewer tests
  3. 3.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  4. 4.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) few child voices in data (d) children speak too fast
  5. 5.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  6. 6.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True
  7. 7.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  8. 8.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  9. 9.Which of these adds to bias? (a) ignore complaints (b) ask different people (c) check who is missing (d) add varied examples
  10. 10.🔍 Which is the odd one out? (a) copy old unfair choices (b) skip testing (c) ignore complaints (d) balance the data
  11. 11.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  12. 12.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  13. 13.🔍 Which is the odd one out? (a) test on one group only (b) use one group only (c) copy old unfair choices (d) check who is missing
  14. 14.🔍 Which is the odd one out? (a) add varied examples (b) ask different people (c) check who is missing (d) skip testing
  15. 15.True or false: “skip testing” helps reduce bias. (a) False (b) True
  16. 16.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its screen size (d) its colour
  17. 17.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  18. 18.Which of these adds to bias? (a) fix wrong labels (b) add varied examples (c) use one group only (d) test on many groups
  19. 19.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  20. 20.🛠️ Does this reduce bias or add to it: test on one group only? (a) adds to bias (b) reduces bias
  21. 21.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  22. 22.True or false: “check who is missing” adds to bias. (a) False (b) True
  23. 23.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  24. 24.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) True (b) False
  25. 25.🛠️ Does this reduce bias or add to it: ask different people? (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.True or false: “use one group only” helps reduce bias. (a) True (b) False
  28. 28.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  29. 29.Which of these adds to bias? (a) balance the data (b) skip testing (c) fix wrong labels (d) add varied examples
  30. 30.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  31. 31.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  32. 32.Which of these helps reduce bias? (a) test on one group only (b) use one group only (c) ignore complaints (d) ask different people
  33. 33.True or false: “skip testing” adds to bias. (a) True (b) False
  34. 34.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
  35. 35.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  36. 36.⚖️ One-sided or balanced data: photos of people from only one age group? (a) balanced data (b) one-sided data
  37. 37.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  38. 38.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  39. 39.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
  40. 40.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) use one group only (d) add varied examples

Answer key

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

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