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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 754 · 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: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True
  2. 2.Which of these helps reduce bias? (a) copy old unfair choices (b) ignore complaints (c) test on many groups (d) skip testing
  3. 3.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) fewer tests (d) more varied data
  4. 4.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  5. 5.Which of these helps reduce bias? (a) copy old unfair choices (b) use one group only (c) test on one group only (d) add varied examples
  6. 6.True or false: “balance the data” adds to bias. (a) False (b) True
  7. 7.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
  8. 8.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  9. 9.True or false: “ask different people” adds to bias. (a) True (b) False
  10. 10.Which of these adds to bias? (a) ask different people (b) copy old unfair choices (c) check who is missing (d) fix wrong labels
  11. 11.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  12. 12.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its screen size (c) its colour (d) its training data
  13. 13.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
  14. 14.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  15. 15.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  16. 16.Which of these helps reduce bias? (a) copy old unfair choices (b) check who is missing (c) ignore complaints (d) skip testing
  17. 17.🔍 Which is the odd one out? (a) test on many groups (b) balance the data (c) skip testing (d) add varied examples
  18. 18.True or false: “use one group only” helps reduce bias. (a) True (b) False
  19. 19.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  20. 20.Which of these helps reduce bias? (a) balance the data (b) use one group only (c) test on one group only (d) copy old unfair choices
  21. 21.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) copy old unfair choices (d) fix wrong labels
  22. 22.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  23. 23.Which of these adds to bias? (a) balance the data (b) ask different people (c) fix wrong labels (d) ignore complaints
  24. 24.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
  25. 25.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
  26. 26.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  27. 27.True or false: “add more of the same” adds to bias. (a) False (b) True
  28. 28.True or false: “test on many groups” adds to bias. (a) False (b) True
  29. 29.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) True (b) False
  30. 30.True or false: “fix wrong labels” adds to bias. (a) True (b) False
  31. 31.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  32. 32.🔍 Which is the odd one out? (a) fix wrong labels (b) ask different people (c) check who is missing (d) test on one group only
  33. 33.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  34. 34.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) one-sided data (b) balanced data
  35. 35.True or false: a fruit sorter shown only ripe mangoes is balanced data (fairer for everyone). (a) False (b) True
  36. 36.Which of these helps reduce bias? (a) skip testing (b) use one group only (c) copy old unfair choices (d) fix wrong labels
  37. 37.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a broken screen (c) a fair model (d) a strong battery
  38. 38.🔍 Which is the odd one out? (a) test on one group only (b) add varied examples (c) use one group only (d) add more of the same
  39. 39.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
  40. 40.True or false: “add varied examples” adds to bias. (a) True (b) False

Answer key

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

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