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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 579 · 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: “check who is missing” adds to bias. (a) True (b) False
  2. 2.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) True (b) False
  3. 3.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  4. 4.🔍 Which is the odd one out? (a) add more of the same (b) test on one group only (c) add varied examples (d) ignore complaints
  5. 5.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  6. 6.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
  7. 7.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  8. 8.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
  9. 9.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
  10. 10.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  11. 11.🛠️ What helps make an AI system fairer? (a) more varied data (b) fewer tests (c) one group only (d) ignoring mistakes
  12. 12.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) a fair model (c) bias from data (d) a strong battery
  13. 13.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  14. 14.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  15. 15.Which of these helps reduce bias? (a) use one group only (b) copy old unfair choices (c) ignore complaints (d) ask different people
  16. 16.True or false: “add varied examples” adds to bias. (a) False (b) True
  17. 17.Which of these helps reduce bias? (a) copy old unfair choices (b) add more of the same (c) test on one group only (d) fix wrong labels
  18. 18.Which of these adds to bias? (a) fix wrong labels (b) check who is missing (c) add varied examples (d) test on one group only
  19. 19.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  20. 20.True or false: a leaf app trained only on summer leaves is balanced data (fairer for everyone). (a) True (b) False
  21. 21.True or false: “ignore complaints” adds to bias. (a) False (b) True
  22. 22.True or false: “test on one group only” helps reduce bias. (a) True (b) False
  23. 23.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
  24. 24.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  25. 25.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  26. 26.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  27. 27.True or false: “skip testing” adds to bias. (a) True (b) False
  28. 28.Which of these adds to bias? (a) copy old unfair choices (b) fix wrong labels (c) test on many groups (d) add varied examples
  29. 29.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  30. 30.🔍 Which is the odd one out? (a) fix wrong labels (b) ask different people (c) add varied examples (d) copy old unfair choices
  31. 31.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  32. 32.🛠️ Does this reduce bias or add to it: check who is missing? (a) reduces bias (b) adds to bias
  33. 33.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  34. 34.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  35. 35.Which of these helps reduce bias? (a) test on one group only (b) ignore complaints (c) use one group only (d) test on many groups
  36. 36.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) skip testing (d) balance the data
  37. 37.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  38. 38.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  39. 39.True or false: “ask different people” adds to bias. (a) True (b) False
  40. 40.True or false: a handwriting app shown only neat writing is one-sided data (likely to make a biased model). (a) False (b) True

Answer key

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

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