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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 276 · 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 helps reduce bias? (a) ignore complaints (b) test on one group only (c) ask different people (d) skip testing
  2. 2.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  3. 3.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
  4. 4.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  5. 5.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) add varied examples (d) add more of the same
  6. 6.True or false: “test on many groups” adds to bias. (a) True (b) False
  7. 7.True or false: “use one group only” adds to bias. (a) False (b) True
  8. 8.Which of these adds to bias? (a) balance the data (b) fix wrong labels (c) test on many groups (d) ignore complaints
  9. 9.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  10. 10.True or false: “skip testing” adds to bias. (a) False (b) True
  11. 11.True or false: “balance the data” helps reduce bias. (a) True (b) False
  12. 12.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  13. 13.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  14. 14.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) it was a rainy day (c) the phone was new (d) children speak too fast
  15. 15.Which of these adds to bias? (a) test on one group only (b) add varied examples (c) check who is missing (d) test on many groups
  16. 16.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
  17. 17.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False
  18. 18.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  19. 19.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  20. 20.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  21. 21.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its screen size (d) its colour
  22. 22.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  23. 23.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  24. 24.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  25. 25.🔍 Which is the odd one out? (a) add varied examples (b) skip testing (c) test on many groups (d) fix wrong labels
  26. 26.🔍 Which is the odd one out? (a) use one group only (b) ignore complaints (c) balance the data (d) copy old unfair choices
  27. 27.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  28. 28.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  29. 29.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  30. 30.True or false: “add more of the same” adds to bias. (a) False (b) True
  31. 31.Which of these helps reduce bias? (a) copy old unfair choices (b) use one group only (c) test on one group only (d) balance the 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 is the odd one out? (a) test on many groups (b) ask different people (c) add varied examples (d) copy old unfair choices
  34. 34.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  35. 35.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
  36. 36.🔍 Which is the odd one out? (a) use one group only (b) add more of the same (c) copy old unfair choices (d) fix wrong labels
  37. 37.🔍 Which is the odd one out? (a) skip testing (b) check who is missing (c) copy old unfair choices (d) use one group only
  38. 38.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  39. 39.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  40. 40.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data

Answer key

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

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