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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 957 · 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: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  2. 2.True or false: “check who is missing” adds to bias. (a) True (b) False
  3. 3.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  4. 4.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) a broken screen (d) bias from data
  5. 5.True or false: “test on many groups” helps reduce bias. (a) False (b) True
  6. 6.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
  7. 7.🔍 Which is the odd one out? (a) balance the data (b) fix wrong labels (c) test on many groups (d) test on one group only
  8. 8.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  9. 9.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  10. 10.🛠️ What helps make an AI system fairer? (a) ignoring mistakes (b) more varied data (c) fewer tests (d) one group only
  11. 11.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
  12. 12.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  13. 13.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
  14. 14.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
  15. 15.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
  16. 16.Which of these helps reduce bias? (a) ignore complaints (b) copy old unfair choices (c) skip testing (d) fix wrong labels
  17. 17.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) add varied examples (d) skip testing
  18. 18.Which of these adds to bias? (a) balance the data (b) add varied examples (c) test on many groups (d) ignore complaints
  19. 19.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  20. 20.True or false: “ignore complaints” adds to bias. (a) True (b) False
  21. 21.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its screen size (d) its training data
  22. 22.True or false: “ask different people” helps reduce bias. (a) True (b) False
  23. 23.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  24. 24.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  25. 25.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  26. 26.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) True (b) False
  27. 27.True or false: “skip testing” adds to bias. (a) False (b) True
  28. 28.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  29. 29.Which of these adds to bias? (a) check who is missing (b) ask different people (c) add varied examples (d) skip testing
  30. 30.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  31. 31.Which of these helps reduce bias? (a) check who is missing (b) copy old unfair choices (c) use one group only (d) skip testing
  32. 32.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  33. 33.🔍 Which is the odd one out? (a) check who is missing (b) use one group only (c) copy old unfair choices (d) test on one group only
  34. 34.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  35. 35.True or false: “fix wrong labels” helps reduce bias. (a) False (b) True
  36. 36.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  37. 37.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
  38. 38.🔍 Which is the odd one out? (a) add more of the same (b) test on one group only (c) balance the data (d) use one group only
  39. 39.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
  40. 40.True or false: “use one group only” helps reduce bias. (a) False (b) True

Answer key

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

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