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AI Bias Worksheet for Kids

AI for students lesson 7: Bias: when data is unfair · Set 652 · 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 voice app trained only on adult voices is balanced data (fairer for everyone). (a) False (b) True
  2. 2.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  3. 3.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  4. 4.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) False (b) True
  5. 5.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  6. 6.True or false: “add varied examples” helps reduce bias. (a) False (b) True
  7. 7.🔍 Which is the odd one out? (a) copy old unfair choices (b) check who is missing (c) add more of the same (d) skip testing
  8. 8.True or false: “balance the data” adds to bias. (a) False (b) True
  9. 9.Which of these adds to bias? (a) add varied examples (b) balance the data (c) fix wrong labels (d) use one group only
  10. 10.🔍 Which is the odd one out? (a) test on one group only (b) fix wrong labels (c) ask different people (d) add varied examples
  11. 11.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  12. 12.🖼️ 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
  13. 13.Which of these helps reduce bias? (a) ignore complaints (b) use one group only (c) test on one group only (d) balance the data
  14. 14.True or false: “add more of the same” helps reduce bias. (a) True (b) False
  15. 15.🔍 Which is the odd one out? (a) copy old unfair choices (b) add varied examples (c) use one group only (d) add more of the same
  16. 16.True or false: “skip testing” adds to bias. (a) True (b) False
  17. 17.Which of these helps reduce bias? (a) skip testing (b) fix wrong labels (c) add more of the same (d) copy old unfair choices
  18. 18.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
  19. 19.True or false: neat and messy writing from many people is balanced data (fairer for everyone). (a) True (b) False
  20. 20.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  21. 21.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  22. 22.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) its training data (d) the weather
  23. 23.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  24. 24.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
  25. 25.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  26. 26.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  27. 27.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
  28. 28.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  29. 29.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
  30. 30.🔍 Which is the odd one out? (a) ignore complaints (b) copy old unfair choices (c) add more of the same (d) fix wrong labels
  31. 31.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  32. 32.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
  33. 33.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
  34. 34.Which of these helps reduce bias? (a) test on one group only (b) add varied examples (c) use one group only (d) skip testing
  35. 35.True or false: “check who is missing” adds to bias. (a) False (b) True
  36. 36.True or false: “test on many groups” helps reduce bias. (a) True (b) False
  37. 37.Which of these helps reduce bias? (a) copy old unfair choices (b) test on many groups (c) use one group only (d) ignore complaints
  38. 38.🔍 Which is the odd one out? (a) test on many groups (b) add more of the same (c) ask different people (d) add varied examples
  39. 39.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) True (b) False
  40. 40.True or false: leaves from every season of the year is one-sided data (likely to make a biased model). (a) True (b) False

Answer key

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

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