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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 984 · 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: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
  2. 2.🔍 Which is the odd one out? (a) add more of the same (b) check who is missing (c) add varied examples (d) ask different people
  3. 3.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  4. 4.True or false: “use one group only” helps reduce bias. (a) True (b) False
  5. 5.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
  6. 6.🖼️ If an image generator always draws a scientist as a man, that shows… (a) bias from data (b) a strong battery (c) a broken screen (d) a fair model
  7. 7.Which of these helps reduce bias? (a) add more of the same (b) ignore complaints (c) use one group only (d) ask different people
  8. 8.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) False (b) True
  9. 9.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  10. 10.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  11. 11.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  12. 12.Which of these adds to bias? (a) check who is missing (b) test on many groups (c) fix wrong labels (d) use one group only
  13. 13.True or false: “check who is missing” helps reduce bias. (a) False (b) True
  14. 14.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
  15. 15.Which of these adds to bias? (a) check who is missing (b) add more of the same (c) balance the data (d) fix wrong labels
  16. 16.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  17. 17.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) the weather (c) its colour (d) its training data
  18. 18.🔍 Which is the odd one out? (a) check who is missing (b) fix wrong labels (c) add varied examples (d) skip testing
  19. 19.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
  20. 20.Which of these adds to bias? (a) test on one group only (b) ask different people (c) add varied examples (d) fix wrong labels
  21. 21.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  22. 22.True or false: “skip testing” adds to bias. (a) True (b) False
  23. 23.True or false: “test on many groups” adds to bias. (a) True (b) False
  24. 24.True or false: “ignore complaints” adds to bias. (a) False (b) True
  25. 25.Which of these helps reduce bias? (a) add varied examples (b) skip testing (c) test on one group only (d) copy old unfair choices
  26. 26.True or false: “use one group only” adds to bias. (a) False (b) True
  27. 27.Which of these helps reduce bias? (a) copy old unfair choices (b) test on many groups (c) add more of the same (d) test on one group only
  28. 28.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
  29. 29.Which of these adds to bias? (a) ask different people (b) add varied examples (c) copy old unfair choices (d) balance the data
  30. 30.🔍 Which is the odd one out? (a) test on many groups (b) ask different people (c) add varied examples (d) copy old unfair choices
  31. 31.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) more varied data (d) fewer tests
  32. 32.🔍 Which is the odd one out? (a) test on one group only (b) use one group only (c) skip testing (d) add varied examples
  33. 33.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  34. 34.🔍 Which is the odd one out? (a) test on one group only (b) skip testing (c) copy old unfair choices (d) check who is missing
  35. 35.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) one-sided data (b) balanced data
  36. 36.True or false: “ask different people” helps reduce bias. (a) False (b) True
  37. 37.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) use one group only (d) fix wrong labels
  38. 38.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  39. 39.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
  40. 40.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias

Answer key

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

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